-
Notifications
You must be signed in to change notification settings - Fork 1
/
results_old.json
14736 lines (14736 loc) · 619 KB
/
results_old.json
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
[
{
"key": "I49GNHEB",
"version": 573,
"library": {
"type": "user",
"id": 7902311,
"name": "supasorn",
"links": {
"alternate": {
"href": "https://www.zotero.org/supasorn",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/users/7902311/items/I49GNHEB",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/supasorn/items/I49GNHEB",
"type": "text/html"
}
},
"meta": {
"creatorSummary": "Karras et al.",
"parsedDate": "2020-03-23",
"numChildren": 2
},
"data": {
"key": "I49GNHEB",
"version": 573,
"itemType": "journalArticle",
"title": "Analyzing and Improving the Image Quality of StyleGAN",
"creators": [
{
"creatorType": "author",
"firstName": "Tero",
"lastName": "Karras"
},
{
"creatorType": "author",
"firstName": "Samuli",
"lastName": "Laine"
},
{
"creatorType": "author",
"firstName": "Miika",
"lastName": "Aittala"
},
{
"creatorType": "author",
"firstName": "Janne",
"lastName": "Hellsten"
},
{
"creatorType": "author",
"firstName": "Jaakko",
"lastName": "Lehtinen"
},
{
"creatorType": "author",
"firstName": "Timo",
"lastName": "Aila"
}
],
"abstractNote": "The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.",
"publicationTitle": "arXiv:1912.04958 [cs, eess, stat]",
"volume": "",
"issue": "",
"pages": "",
"date": "2020-03-23",
"series": "",
"seriesTitle": "",
"seriesText": "",
"journalAbbreviation": "",
"language": "",
"DOI": "",
"ISSN": "",
"shortTitle": "",
"url": "http://arxiv.org/abs/1912.04958",
"accessDate": "2021-09-27T11:21:13Z",
"archive": "",
"archiveLocation": "",
"libraryCatalog": "arXiv.org",
"callNumber": "",
"rights": "",
"extra": "arXiv: 1912.04958",
"tags": [
{
"tag": "Computer Science - Computer Vision and Pattern Recognition",
"type": 1
},
{
"tag": "Computer Science - Machine Learning",
"type": 1
},
{
"tag": "Computer Science - Neural and Evolutionary Computing",
"type": 1
},
{
"tag": "Electrical Engineering and Systems Science - Image and Video Processing",
"type": 1
},
{
"tag": "Statistics - Machine Learning",
"type": 1
}
],
"collections": [],
"relations": {},
"dateAdded": "2021-09-27T11:21:13Z",
"dateModified": "2021-09-27T11:23:19Z"
}
},
{
"key": "56VXC5I9",
"version": 568,
"library": {
"type": "user",
"id": 7902311,
"name": "supasorn",
"links": {
"alternate": {
"href": "https://www.zotero.org/supasorn",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/users/7902311/items/56VXC5I9",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/supasorn/items/56VXC5I9",
"type": "text/html"
}
},
"meta": {
"creatorSummary": "Vahdat et al.",
"parsedDate": "2021-06-10",
"numChildren": 2
},
"data": {
"key": "56VXC5I9",
"version": 568,
"itemType": "journalArticle",
"title": "Score-based Generative Modeling in Latent Space",
"creators": [
{
"creatorType": "author",
"firstName": "Arash",
"lastName": "Vahdat"
},
{
"creatorType": "author",
"firstName": "Karsten",
"lastName": "Kreis"
},
{
"creatorType": "author",
"firstName": "Jan",
"lastName": "Kautz"
}
],
"abstractNote": "Score-based generative models (SGMs) have recently demonstrated impressive results in terms of both sample quality and distribution coverage. However, they are usually applied directly in data space and often require thousands of network evaluations for sampling. Here, we propose the Latent Score-based Generative Model (LSGM), a novel approach that trains SGMs in a latent space, relying on the variational autoencoder framework. Moving from data to latent space allows us to train more expressive generative models, apply SGMs to non-continuous data, and learn smoother SGMs in a smaller space, resulting in fewer network evaluations and faster sampling. To enable training LSGMs end-to-end in a scalable and stable manner, we (i) introduce a new score-matching objective suitable to the LSGM setting, (ii) propose a novel parameterization of the score function that allows SGM to focus on the mismatch of the target distribution with respect to a simple Normal one, and (iii) analytically derive multiple techniques for variance reduction of the training objective. LSGM obtains a state-of-the-art FID score of 2.10 on CIFAR-10, outperforming all existing generative results on this dataset. On CelebA-HQ-256, LSGM is on a par with previous SGMs in sample quality while outperforming them in sampling time by two orders of magnitude. In modeling binary images, LSGM achieves state-of-the-art likelihood on the binarized OMNIGLOT dataset.",
"publicationTitle": "arXiv:2106.05931 [cs, stat]",
"volume": "",
"issue": "",
"pages": "",
"date": "2021-06-10",
"series": "",
"seriesTitle": "",
"seriesText": "",
"journalAbbreviation": "",
"language": "",
"DOI": "",
"ISSN": "",
"shortTitle": "",
"url": "http://arxiv.org/abs/2106.05931",
"accessDate": "2021-09-27T09:13:45Z",
"archive": "",
"archiveLocation": "",
"libraryCatalog": "arXiv.org",
"callNumber": "",
"rights": "",
"extra": "arXiv: 2106.05931",
"tags": [
{
"tag": "Computer Science - Machine Learning",
"type": 1
},
{
"tag": "Statistics - Machine Learning",
"type": 1
}
],
"collections": [
"TNWL7M5C"
],
"relations": {},
"dateAdded": "2021-09-27T09:13:45Z",
"dateModified": "2021-09-27T09:13:46Z"
}
},
{
"key": "2V993BEV",
"version": 572,
"library": {
"type": "user",
"id": 7902311,
"name": "supasorn",
"links": {
"alternate": {
"href": "https://www.zotero.org/supasorn",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/users/7902311/items/2V993BEV",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/supasorn/items/2V993BEV",
"type": "text/html"
}
},
"meta": {
"creatorSummary": "Federici et al.",
"parsedDate": "2020-02-18",
"numChildren": 2
},
"data": {
"key": "2V993BEV",
"version": 572,
"itemType": "journalArticle",
"title": "Learning Robust Representations via Multi-View Information Bottleneck",
"creators": [
{
"creatorType": "author",
"firstName": "Marco",
"lastName": "Federici"
},
{
"creatorType": "author",
"firstName": "Anjan",
"lastName": "Dutta"
},
{
"creatorType": "author",
"firstName": "Patrick",
"lastName": "Forré"
},
{
"creatorType": "author",
"firstName": "Nate",
"lastName": "Kushman"
},
{
"creatorType": "author",
"firstName": "Zeynep",
"lastName": "Akata"
}
],
"abstractNote": "The information bottleneck principle provides an information-theoretic method for representation learning, by training an encoder to retain all information which is relevant for predicting the label while minimizing the amount of other, excess information in the representation. The original formulation, however, requires labeled data to identify the superfluous information. In this work, we extend this ability to the multi-view unsupervised setting, where two views of the same underlying entity are provided but the label is unknown. This enables us to identify superfluous information as that not shared by both views. A theoretical analysis leads to the definition of a new multi-view model that produces state-of-the-art results on the Sketchy dataset and label-limited versions of the MIR-Flickr dataset. We also extend our theory to the single-view setting by taking advantage of standard data augmentation techniques, empirically showing better generalization capabilities when compared to common unsupervised approaches for representation learning.",
"publicationTitle": "arXiv:2002.07017 [cs, stat]",
"volume": "",
"issue": "",
"pages": "",
"date": "2020-02-18",
"series": "",
"seriesTitle": "",
"seriesText": "",
"journalAbbreviation": "",
"language": "",
"DOI": "",
"ISSN": "",
"shortTitle": "",
"url": "http://arxiv.org/abs/2002.07017",
"accessDate": "2021-09-27T08:47:30Z",
"archive": "",
"archiveLocation": "",
"libraryCatalog": "arXiv.org",
"callNumber": "",
"rights": "",
"extra": "arXiv: 2002.07017",
"tags": [
{
"tag": "Computer Science - Machine Learning",
"type": 1
},
{
"tag": "Statistics - Machine Learning",
"type": 1
}
],
"collections": [],
"relations": {
"owl:sameAs": "http://zotero.org/groups/4320173/items/HT2HBU8C"
},
"dateAdded": "2021-09-27T08:47:31Z",
"dateModified": "2021-09-27T08:47:31Z"
}
},
{
"key": "V8LURJYZ",
"version": 562,
"library": {
"type": "user",
"id": 7902311,
"name": "supasorn",
"links": {
"alternate": {
"href": "https://www.zotero.org/supasorn",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/users/7902311/items/V8LURJYZ",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/supasorn/items/V8LURJYZ",
"type": "text/html"
}
},
"meta": {
"creatorSummary": "Tishby et al.",
"parsedDate": "2000-04-24",
"numChildren": 2
},
"data": {
"key": "V8LURJYZ",
"version": 562,
"itemType": "journalArticle",
"title": "The information bottleneck method",
"creators": [
{
"creatorType": "author",
"firstName": "Naftali",
"lastName": "Tishby"
},
{
"creatorType": "author",
"firstName": "Fernando C.",
"lastName": "Pereira"
},
{
"creatorType": "author",
"firstName": "William",
"lastName": "Bialek"
}
],
"abstractNote": "We define the relevant information in a signal $x\\in X$ as being the information that this signal provides about another signal $y\\in \\Y$. Examples include the information that face images provide about the names of the people portrayed, or the information that speech sounds provide about the words spoken. Understanding the signal $x$ requires more than just predicting $y$, it also requires specifying which features of $\\X$ play a role in the prediction. We formalize this problem as that of finding a short code for $\\X$ that preserves the maximum information about $\\Y$. That is, we squeeze the information that $\\X$ provides about $\\Y$ through a `bottleneck' formed by a limited set of codewords $\\tX$. This constrained optimization problem can be seen as a generalization of rate distortion theory in which the distortion measure $d(x,\\x)$ emerges from the joint statistics of $\\X$ and $\\Y$. This approach yields an exact set of self consistent equations for the coding rules $X \\to \\tX$ and $\\tX \\to \\Y$. Solutions to these equations can be found by a convergent re-estimation method that generalizes the Blahut-Arimoto algorithm. Our variational principle provides a surprisingly rich framework for discussing a variety of problems in signal processing and learning, as will be described in detail elsewhere.",
"publicationTitle": "arXiv:physics/0004057",
"volume": "",
"issue": "",
"pages": "",
"date": "2000-04-24",
"series": "",
"seriesTitle": "",
"seriesText": "",
"journalAbbreviation": "",
"language": "",
"DOI": "",
"ISSN": "",
"shortTitle": "",
"url": "http://arxiv.org/abs/physics/0004057",
"accessDate": "2021-09-27T07:54:33Z",
"archive": "",
"archiveLocation": "",
"libraryCatalog": "arXiv.org",
"callNumber": "",
"rights": "",
"extra": "arXiv: physics/0004057",
"tags": [
{
"tag": "Computer Science - Machine Learning",
"type": 1
},
{
"tag": "Condensed Matter - Disordered Systems and Neural Networks",
"type": 1
},
{
"tag": "Nonlinear Sciences - Adaptation and Self-Organizing Systems",
"type": 1
},
{
"tag": "Physics - Data Analysis, Statistics and Probability",
"type": 1
}
],
"collections": [],
"relations": {},
"dateAdded": "2021-09-27T07:54:33Z",
"dateModified": "2021-09-27T07:54:33Z"
}
},
{
"key": "RT7KAHYC",
"version": 572,
"library": {
"type": "user",
"id": 7902311,
"name": "supasorn",
"links": {
"alternate": {
"href": "https://www.zotero.org/supasorn",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/users/7902311/items/RT7KAHYC",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/supasorn/items/RT7KAHYC",
"type": "text/html"
}
},
"meta": {
"creatorSummary": "Tian et al.",
"parsedDate": "2021-04-06",
"numChildren": 2
},
"data": {
"key": "RT7KAHYC",
"version": 572,
"itemType": "journalArticle",
"title": "Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification",
"creators": [
{
"creatorType": "author",
"firstName": "Xudong",
"lastName": "Tian"
},
{
"creatorType": "author",
"firstName": "Zhizhong",
"lastName": "Zhang"
},
{
"creatorType": "author",
"firstName": "Shaohui",
"lastName": "Lin"
},
{
"creatorType": "author",
"firstName": "Yanyun",
"lastName": "Qu"
},
{
"creatorType": "author",
"firstName": "Yuan",
"lastName": "Xie"
},
{
"creatorType": "author",
"firstName": "Lizhuang",
"lastName": "Ma"
}
],
"abstractNote": "The Information Bottleneck (IB) provides an information theoretic principle for representation learning, by retaining all information relevant for predicting label while minimizing the redundancy. Though IB principle has been applied to a wide range of applications, its optimization remains a challenging problem which heavily relies on the accurate estimation of mutual information. In this paper, we present a new strategy, Variational Self-Distillation (VSD), which provides a scalable, flexible and analytic solution to essentially fitting the mutual information but without explicitly estimating it. Under rigorously theoretical guarantee, VSD enables the IB to grasp the intrinsic correlation between representation and label for supervised training. Furthermore, by extending VSD to multi-view learning, we introduce two other strategies, Variational Cross-Distillation (VCD) and Variational Mutual-Learning (VML), which significantly improve the robustness of representation to view-changes by eliminating view-specific and task-irrelevant information. To verify our theoretically grounded strategies, we apply our approaches to cross-modal person Re-ID, and conduct extensive experiments, where the superior performance against state-of-the-art methods are demonstrated. Our intriguing findings highlight the need to rethink the way to estimate mutual",
"publicationTitle": "arXiv:2104.02862 [cs]",
"volume": "",
"issue": "",
"pages": "",
"date": "2021-04-06",
"series": "",
"seriesTitle": "",
"seriesText": "",
"journalAbbreviation": "",
"language": "",
"DOI": "",
"ISSN": "",
"shortTitle": "Farewell to Mutual Information",
"url": "http://arxiv.org/abs/2104.02862",
"accessDate": "2021-09-27T07:36:49Z",
"archive": "",
"archiveLocation": "",
"libraryCatalog": "arXiv.org",
"callNumber": "",
"rights": "",
"extra": "arXiv: 2104.02862",
"tags": [
{
"tag": "Computer Science - Computer Vision and Pattern Recognition",
"type": 1
}
],
"collections": [],
"relations": {
"owl:sameAs": "http://zotero.org/groups/4320173/items/KVPWSBSJ"
},
"dateAdded": "2021-09-27T07:36:49Z",
"dateModified": "2021-09-27T07:37:26Z"
}
},
{
"key": "2ZXDF7SP",
"version": 554,
"library": {
"type": "user",
"id": 7902311,
"name": "supasorn",
"links": {
"alternate": {
"href": "https://www.zotero.org/supasorn",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/users/7902311/items/2ZXDF7SP",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/supasorn/items/2ZXDF7SP",
"type": "text/html"
}
},
"meta": {
"numChildren": 0
},
"data": {
"key": "2ZXDF7SP",
"version": 554,
"itemType": "attachment",
"linkMode": "imported_url",
"title": "information.pdf",
"accessDate": "2021-09-27T06:46:43Z",
"url": "https://www.princeton.edu/~cuff/ele201/kulkarni_text/information.pdf",
"note": "",
"contentType": "application/pdf",
"charset": "",
"filename": "information.pdf",
"md5": "c9c163d05a47dd760ede002eedd5e372",
"mtime": 1632725203000,
"tags": [],
"collections": [],
"relations": {},
"dateAdded": "2021-09-27T06:46:43Z",
"dateModified": "2021-09-27T06:46:43Z"
}
},
{
"key": "LI4EMUQM",
"version": 546,
"library": {
"type": "user",
"id": 7902311,
"name": "supasorn",
"links": {
"alternate": {
"href": "https://www.zotero.org/supasorn",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/users/7902311/items/LI4EMUQM",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/supasorn/items/LI4EMUQM",
"type": "text/html"
}
},
"meta": {
"creatorSummary": "Vahdat et al.",
"parsedDate": "2021-06-10",
"numChildren": 2
},
"data": {
"key": "LI4EMUQM",
"version": 546,
"itemType": "journalArticle",
"title": "Score-based Generative Modeling in Latent Space",
"creators": [
{
"creatorType": "author",
"firstName": "Arash",
"lastName": "Vahdat"
},
{
"creatorType": "author",
"firstName": "Karsten",
"lastName": "Kreis"
},
{
"creatorType": "author",
"firstName": "Jan",
"lastName": "Kautz"
}
],
"abstractNote": "Score-based generative models (SGMs) have recently demonstrated impressive results in terms of both sample quality and distribution coverage. However, they are usually applied directly in data space and often require thousands of network evaluations for sampling. Here, we propose the Latent Score-based Generative Model (LSGM), a novel approach that trains SGMs in a latent space, relying on the variational autoencoder framework. Moving from data to latent space allows us to train more expressive generative models, apply SGMs to non-continuous data, and learn smoother SGMs in a smaller space, resulting in fewer network evaluations and faster sampling. To enable training LSGMs end-to-end in a scalable and stable manner, we (i) introduce a new score-matching objective suitable to the LSGM setting, (ii) propose a novel parameterization of the score function that allows SGM to focus on the mismatch of the target distribution with respect to a simple Normal one, and (iii) analytically derive multiple techniques for variance reduction of the training objective. LSGM obtains a state-of-the-art FID score of 2.10 on CIFAR-10, outperforming all existing generative results on this dataset. On CelebA-HQ-256, LSGM is on a par with previous SGMs in sample quality while outperforming them in sampling time by two orders of magnitude. In modeling binary images, LSGM achieves state-of-the-art likelihood on the binarized OMNIGLOT dataset.",
"publicationTitle": "arXiv:2106.05931 [cs, stat]",
"volume": "",
"issue": "",
"pages": "",
"date": "2021-06-10",
"series": "",
"seriesTitle": "",
"seriesText": "",
"journalAbbreviation": "",
"language": "",
"DOI": "",
"ISSN": "",
"shortTitle": "",
"url": "http://arxiv.org/abs/2106.05931",
"accessDate": "2021-09-27T05:17:53Z",
"archive": "",
"archiveLocation": "",
"libraryCatalog": "arXiv.org",
"callNumber": "",
"rights": "",
"extra": "arXiv: 2106.05931",
"tags": [
{
"tag": "Computer Science - Machine Learning",
"type": 1
},
{
"tag": "Statistics - Machine Learning",
"type": 1
}
],
"collections": [
"TNWL7M5C"
],
"relations": {},
"dateAdded": "2021-09-27T05:17:53Z",
"dateModified": "2021-09-27T05:17:53Z"
}
},
{
"key": "AKAHQUX3",
"version": 546,
"library": {
"type": "user",
"id": 7902311,
"name": "supasorn",
"links": {
"alternate": {
"href": "https://www.zotero.org/supasorn",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/users/7902311/items/AKAHQUX3",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/supasorn/items/AKAHQUX3",
"type": "text/html"
}
},
"meta": {
"creatorSummary": "Kong and Ping",
"parsedDate": "2021-06-23",
"numChildren": 3
},
"data": {
"key": "AKAHQUX3",
"version": 546,
"itemType": "journalArticle",
"title": "On Fast Sampling of Diffusion Probabilistic Models",
"creators": [
{
"creatorType": "author",
"firstName": "Zhifeng",
"lastName": "Kong"
},
{
"creatorType": "author",
"firstName": "Wei",
"lastName": "Ping"
}
],
"abstractNote": "In this work, we propose FastDPM, a unified framework for fast sampling in diffusion probabilistic models. FastDPM generalizes previous methods and gives rise to new algorithms with improved sample quality. We systematically investigate the fast sampling methods under this framework across different domains, on different datasets, and with different amount of conditional information provided for generation. We find the performance of a particular method depends on data domains (e.g., image or audio), the trade-off between sampling speed and sample quality, and the amount of conditional information. We further provide insights and recipes on the choice of methods for practitioners.",
"publicationTitle": "arXiv:2106.00132 [cs]",
"volume": "",
"issue": "",
"pages": "",
"date": "2021-06-23",
"series": "",
"seriesTitle": "",
"seriesText": "",
"journalAbbreviation": "",
"language": "",
"DOI": "",
"ISSN": "",
"shortTitle": "",
"url": "http://arxiv.org/abs/2106.00132",
"accessDate": "2021-09-27T05:17:50Z",
"archive": "",
"archiveLocation": "",
"libraryCatalog": "arXiv.org",
"callNumber": "",
"rights": "",
"extra": "arXiv: 2106.00132",
"tags": [
{
"tag": "Computer Science - Machine Learning",
"type": 1
}
],
"collections": [
"TNWL7M5C"
],
"relations": {},
"dateAdded": "2021-09-27T05:17:50Z",
"dateModified": "2021-09-27T05:17:50Z"
}
},
{
"key": "9LNPDQAR",
"version": 543,
"library": {
"type": "user",
"id": 7902311,
"name": "supasorn",
"links": {
"alternate": {
"href": "https://www.zotero.org/supasorn",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/users/7902311/items/9LNPDQAR",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/supasorn/items/9LNPDQAR",
"type": "text/html"
}
},
"meta": {
"creatorSummary": "Tian et al.",
"numChildren": 1
},
"data": {
"key": "9LNPDQAR",
"version": 543,
"itemType": "journalArticle",
"title": "Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification",
"creators": [
{
"creatorType": "author",
"firstName": "Xudong",
"lastName": "Tian"
},
{
"creatorType": "author",
"firstName": "Zhizhong",
"lastName": "Zhang"
},
{
"creatorType": "author",
"firstName": "Shaohui",
"lastName": "Lin"
},
{
"creatorType": "author",
"firstName": "Yanyun",
"lastName": "Qu"
},
{
"creatorType": "author",
"firstName": "Yuan",
"lastName": "Xie"
},
{
"creatorType": "author",
"firstName": "Lizhuang",
"lastName": "Ma"
}
],
"abstractNote": "The Information Bottleneck (IB) provides an information theoretic principle for representation learning, by retaining all information relevant for predicting label while minimizing the redundancy. Though IB principle has been applied to a wide range of applications, its optimization remains a challenging problem which heavily relies on the accurate estimation of mutual information. In this paper, we present a new strategy, Variational Self-Distillation (VSD), which provides a scalable, flexible and analytic solution to essentially fitting the mutual information but without explicitly estimating it. Under rigorously theoretical guarantee, VSD enables the IB to grasp the intrinsic correlation between representation and label for supervised training. Furthermore, by extending VSD to multi-view learning, we introduce two other strategies, Variational Cross-Distillation (VCD) and Variational Mutual-Learning (VML), which significantly improve the robustness of representation to viewchanges by eliminating view-specific and task-irrelevant information. To verify our theoretically grounded strategies, we apply our approaches to cross-modal person Re-ID, and conduct extensive experiments, where the superior performance against state-of-the-art methods are demonstrated. Our intriguing findings highlight the need to rethink the way to estimate mutual information.",
"publicationTitle": "",
"volume": "",
"issue": "",
"pages": "10",
"date": "",
"series": "",
"seriesTitle": "",
"seriesText": "",
"journalAbbreviation": "",
"language": "en",
"DOI": "",
"ISSN": "",
"shortTitle": "",
"url": "",
"accessDate": "",
"archive": "",
"archiveLocation": "",
"libraryCatalog": "Zotero",
"callNumber": "",
"rights": "",
"extra": "",
"tags": [],
"collections": [],
"relations": {},
"dateAdded": "2021-09-16T10:21:00Z",
"dateModified": "2021-09-16T10:21:00Z"
}
},
{
"key": "MXZBFTHW",
"version": 539,
"library": {
"type": "user",
"id": 7902311,
"name": "supasorn",
"links": {
"alternate": {
"href": "https://www.zotero.org/supasorn",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/users/7902311/items/MXZBFTHW",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/supasorn/items/MXZBFTHW",
"type": "text/html"
}
},
"meta": {
"creatorSummary": "Nachmani et al.",
"parsedDate": "2021-06-14",
"numChildren": 2
},
"data": {
"key": "MXZBFTHW",
"version": 539,
"itemType": "journalArticle",
"title": "Non Gaussian Denoising Diffusion Models",
"creators": [
{
"creatorType": "author",
"firstName": "Eliya",
"lastName": "Nachmani"
},
{
"creatorType": "author",
"firstName": "Robin San",
"lastName": "Roman"
},
{
"creatorType": "author",
"firstName": "Lior",
"lastName": "Wolf"
}
],
"abstractNote": "Generative diffusion processes are an emerging and effective tool for image and speech generation. In the existing methods, the underline noise distribution of the diffusion process is Gaussian noise. However, fitting distributions with more degrees of freedom, could help the performance of such generative models. In this work, we investigate other types of noise distribution for the diffusion process. Specifically, we show that noise from Gamma distribution provides improved results for image and speech generation. Moreover, we show that using a mixture of Gaussian noise variables in the diffusion process improves the performance over a diffusion process that is based on a single distribution. Our approach preserves the ability to efficiently sample state in the training diffusion process while using Gamma noise and a mixture of noise.",
"publicationTitle": "arXiv:2106.07582 [cs, eess]",
"volume": "",
"issue": "",
"pages": "",
"date": "2021-06-14",
"series": "",
"seriesTitle": "",
"seriesText": "",
"journalAbbreviation": "",
"language": "",
"DOI": "",
"ISSN": "",
"shortTitle": "",
"url": "http://arxiv.org/abs/2106.07582",
"accessDate": "2021-09-14T12:15:47Z",
"archive": "",
"archiveLocation": "",
"libraryCatalog": "arXiv.org",
"callNumber": "",
"rights": "",
"extra": "arXiv: 2106.07582",
"tags": [
{
"tag": "Computer Science - Computer Vision and Pattern Recognition",
"type": 1
},
{
"tag": "Computer Science - Machine Learning",
"type": 1
},
{
"tag": "Computer Science - Sound",
"type": 1
},
{
"tag": "Electrical Engineering and Systems Science - Audio and Speech Processing",
"type": 1
}
],
"collections": [
"TNWL7M5C"
],
"relations": {},
"dateAdded": "2021-09-14T12:15:47Z",
"dateModified": "2021-09-14T12:16:00Z"
}
},
{
"key": "HXNUEG2D",
"version": 539,
"library": {
"type": "user",
"id": 7902311,
"name": "supasorn",
"links": {
"alternate": {
"href": "https://www.zotero.org/supasorn",
"type": "text/html"
}
}
},
"links": {
"self": {
"href": "https://api.zotero.org/users/7902311/items/HXNUEG2D",
"type": "application/json"
},
"alternate": {
"href": "https://www.zotero.org/supasorn/items/HXNUEG2D",
"type": "text/html"
}
},
"meta": {
"creatorSummary": "Lam et al.",
"parsedDate": "2021-08-31",
"numChildren": 2
},
"data": {
"key": "HXNUEG2D",
"version": 539,
"itemType": "journalArticle",
"title": "Bilateral Denoising Diffusion Models",
"creators": [
{
"creatorType": "author",
"firstName": "Max W. Y.",
"lastName": "Lam"
},
{
"creatorType": "author",
"firstName": "Jun",
"lastName": "Wang"
},
{
"creatorType": "author",
"firstName": "Rongjie",
"lastName": "Huang"
},
{
"creatorType": "author",
"firstName": "Dan",
"lastName": "Su"
},
{
"creatorType": "author",
"firstName": "Dong",
"lastName": "Yu"
}
],
"abstractNote": "Denoising diffusion probabilistic models (DDPMs) have emerged as competitive generative models yet brought challenges to efficient sampling. In this paper, we propose novel bilateral denoising diffusion models (BDDMs), which take significantly fewer steps to generate high-quality samples. From a bilateral modeling objective, BDDMs parameterize the forward and reverse processes with a score network and a scheduling network, respectively. We show that a new lower bound tighter than the standard evidence lower bound can be derived as a surrogate objective for training the two networks. In particular, BDDMs are efficient, simple-to-train, and capable of further improving any pre-trained DDPM by optimizing the inference noise schedules. Our experiments demonstrated that BDDMs can generate high-fidelity samples with as few as 3 sampling steps and produce comparable or even higher quality samples than DDPMs using 1000 steps with only 16 sampling steps (a 62x speedup).",
"publicationTitle": "arXiv:2108.11514 [cs, eess]",
"volume": "",
"issue": "",
"pages": "",
"date": "2021-08-31",
"series": "",
"seriesTitle": "",
"seriesText": "",
"journalAbbreviation": "",
"language": "",
"DOI": "",
"ISSN": "",
"shortTitle": "",
"url": "http://arxiv.org/abs/2108.11514",
"accessDate": "2021-09-14T12:15:45Z",
"archive": "",