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    <h3>Introduction</h3>
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      <li>간단하게 paper 정리.</li>
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    <h3>Papers</h3>
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      <li><a href="{{ site.baseurl }}/docs/google_inception">Google Inception Models.</a></li>
      <li><a href="{{ site.baseurl }}/docs/synthetic_gradients">Decoupled Neural Interfaces using Synthetic Gradients.</a></li>
      <li><a href="{{ site.baseurl }}/docs/neural_turing_machine">Neural Turing Machine.</a></li>
      <li><a href="{{ site.baseurl }}/docs/google_neural_machine_translation">Google Neural Machine Translation System.</a></li>
      <li><a href="{{ site.baseurl }}/docs/swivel">Swivel: Improving Embeddings by Noticing What's Missing.</a></li>
      <li><a href="{{ site.baseurl }}/docs/attention_ocr">Attention-based Extraction of Structured Information from Street View Imagery.</a></li>
      <li><a href="{{ site.baseurl }}/docs/fairseq">Convolutional Sequence to Sequence Learning</a></li>
      <li><a href="{{ site.baseurl }}/docs/deepimgir">Deep Image Retrival</a></li>
      <li><a href="{{ site.baseurl }}/docs/noisy_large_scale_dataset">Learning From Noisy Large-Scale Datasets With Minimal Supervision</a></li>
      <li><a href="{{ site.baseurl }}/docs/building_mobile_app_with_tf">Building Mobile Applications with TensorFlow</a></li>
      <li><a href="{{ site.baseurl }}/docs/deep_metric_learning_via_facility_location">Deep Metric Learning via Facility Location</a></li>
      <li><a href="{{ site.baseurl }}/docs/delf">Large-Scale Image Retrieval with Attentive Deep Local Features</a></li>
      <li><a href="{{ site.baseurl }}/docs/poor_convolution_network">Why do deep convolutional networks generalize so poorly to small image transformations?</a></li>
      <li><a href="{{ site.baseurl }}/docs/bam_and_cbam">CBAM: Convolutional Block Attention Module</a></li>
      <li><a href="{{ site.baseurl }}/docs/wide_resnet">Wide Resnet</a></li>
      <li><a href="{{ site.baseurl }}/docs/dropblock">DropBlock: A regularization method for convolution networks</a></li>
      <li><a href="{{ site.baseurl }}/docs/bag_of_tricks_for_image_classification">Bag of tricks for image classification w/ CNN</a></li>
      <li><a href="{{ site.baseurl }}/docs/domain_adaptive_transfer_learning_with_specialist_model">Domain adaptive transfer learning w/ specialist model</a></li>
      <li><a href="{{ site.baseurl }}/docs/do_better_imagenet_models_transfer_better">Do better ImageNet models transfer better?</a></li>
      <li><a href="{{ site.baseurl }}/docs/efficient_net">EfficientNet: Rethinking Model Scaling for CNN.</a></li>
      <li><a href="{{ site.baseurl }}/docs/arcface">ArcFace: Additive Angular Margin Loss for Deep Face Recognition.</a></li>
      <li><a href="{{ site.baseurl }}/docs/billon_scale_semi_supervised_learning">Billion-scale semi-supervised learning for image classification.</a></li>      
      <li><a href="{{ site.baseurl }}/docs/multigrain">MultiGrain: A unified image embedding for classes and instances.</a></li>
      <li><a href="{{ site.baseurl }}/docs/hidden_technical_debt">Hidden Technical Debt in Machine Learning Systems.</a></li>
      <li><a href="{{ site.baseurl }}/docs/mce_mfr">Benchmarking neural network robustness to common corruptions and perturbations.</a></li>
      <li><a href="{{ site.baseurl }}/docs/assembled_cnn">Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network.</a></li>
      <li><a href="{{ site.baseurl }}/docs/ltr_images_with_cross_model_gcn">Learning to Rank Images with Cross-Modal Graph Convolutions.</a></li>
      <!-- <li><a href="{{ site.baseurl }}/docs/pinterest_image_search">Demystifying Core Ranking in Pinterest Image Search.</a></li> -->
      <li><a href="{{ site.baseurl }}/docs/are_we_done_with_imagenet">Are we done with ImageNet?</a></li>            
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