This repository contains short summaries of some machine learning papers. Original repo: https://github.com/aleju/papers
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UNSUPERVISED LEARNING** **ECCV 2018** Deep Clustering for Unsupervised Learning of Visual Features - **
OBJECT DETECTION** **POINT CLOUD** **SELF-DRIVING CARS** **ECCV 2018** Deep Continuous Fusion for Multi-Sensor 3D Object Detection - **
AUDIO** **SOUND SOURCE LOCALIZATION** **ACTION RECOGNITION** **SOUND SOURCE SEPARATION** **SELF-SUPERVISED** **ECCV 2018** Audio-Visual Scene Analysis with Self-Supervised Multisensory Features - **
UNCERTAINTY** **ECCV 2018** Towards Realistic Predictors - **
OBJECT DETECTION** **ECCV 2018** Acquisition of Localization Confidence for Accurate Object Detection - **
OBJECT DETECTION** **ECCV 2018** CornerNet: Detecting Objects as Paired Keypoints - **
NORMALIZATION** **ECCV 2018** Group Normalization - **
ARCHITECTURES** **ATTENTION** **ECCV 2018** Convolutional Networks with Adaptive Inference Graphs
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ARCHITECTURES** **ATTENTION** Spatial Transformer Networks (thanks, alexobednikov)
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LOSS FUNCTIONS** **RECOGNITION** Working hard to know your neighbor’s margins: Local descriptor learning loss (thanks, alexobednikov)
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FACE RECOGNITION** **FACES** Neural Aggregation Network for Video Face Recognition (thanks, alexobednikov)
- Critical Learning Periods in Deep Neural Networks
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GAN** **SELF-DRIVING CARS** High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs - **
SELF-DRIVING CARS** Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art
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SELF-DRIVING CARS** Systematic Testing of Convolutional Neural Networks for Autonomous Driving - **
SELF-DRIVING CARS** **SEGMENTATION** Fast Scene Understanding for Autonomous Driving - **
SELF-DRIVING CARS** Arguing Machines: Perception-Control System Redundancy and Edge Case Discovery in Real-World Autonomous Driving - **
SELF-DRIVING CARS** **GAN** **REINFORCEMENT** Virtual to Real Reinforcement Learning for Autonomous Driving - **
SELF-DRIVING CARS** End to End Learning for Self-Driving Cars
- Snapshot Ensembles: Train 1, get M for free
- Image Crowd Counting Using Convolutional Neural Network and Markov Random Field
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REINFORCEMENT** Rainbow: Combining Improvements in Deep Reinforcement Learning - **
REINFORCEMENT** Learning to Navigate in Complex Environments - **
GAN** Unsupervised Image-to-Image Translation Networks - **
RNN** Dilated Recurrent Neural Networks - **
OBJECT DETECTION** **TRACKING** Detect to Track and Track to Detect - **
ARCHITECTURES** Dilated Residual Networks
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OBJECT DETECTION** Feature Pyramid Networks for Object Detection - **
OBJECT DETECTION** SSD: Single Shot MultiBox Detector - **
OBJECT DETECTION** **EFFICIENT NETWORKS** MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications - **
OBJECT DETECTION** Mask R-CNN
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FACES** Multi-view Face Detection Using Deep Convolutional Neural Networks (aka DDFD) (thanks, arnaldog12)
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GAN** On the Effects of Batch and Weight Normalization in Generative Adversarial Networks - **
GAN** BEGAN - **
GAN** StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks - **
ACTIVATION FUNCTIONS** Self-Normalizing Neural Networks - **
GAN** Wasserstein GAN (aka WGAN)
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OBJECT DETECTION** YOLO9000: Better, Faster, Stronger (aka YOLOv2) - **
OBJECT DETECTION** You Only Look Once: Unified, Real-Time Object Detection (aka YOLO) - **
OBJECT DETECTION** PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
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OBJECT DETECTION** R-FCN: Object Detection via Region-based Fully Convolutional Networks - **
OBJECT DETECTION** Faster R-CNN - **
OBJECT DETECTION** Fast R-CNN - **
OBJECT DETECTION** Rich feature hierarchies for accurate object detection and semantic segmentation (aka R-CNN) - **
PEDESTRIANS** Ten Years of Pedestrian Detection, What Have We Learned? - **
NEURAL STYLE** Instance Normalization: The Missing Ingredient for Fast Stylization
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HUMAN POSE ESTIMATION** Stacked Hourglass Networks for Human Pose Estimation - **
FACES** DeepFace: Closing the Gap to Human-Level Performance in Face Verification - **
TRANSLATION** Character-based Neural Machine Translation
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HUMAN POSE ESTIMATION** Convolutional Pose Machines - **
FACES** HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition - **
FACES** Face Attribute Prediction Using Off-the-Shelf CNN Features - **
FACES** CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection - Conditional Image Generation with PixelCNN Decoders
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GAN** InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets - **
GAN** Improved Techniques for Training GANs - Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
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ARCHITECTURES** FractalNet: Ultra-Deep Neural Networks without Residuals - PlaNet - Photo Geolocation with Convolutional Neural Networks
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OPTIMIZERS** Adam: A Method for Stochastic Optimization - **
GAN** **RNN** Generating images with recurrent adversarial networks - **
GAN** Adversarially Learned Inference
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ARCHITECTURES** Resnet in Resnet: Generalizing Residual Architectures - **
AUTOENCODERS** Rank Ordered Autoencoders - **
ARCHITECTURES** Wide Residual Networks - **
ARCHITECTURES** Identity Mappings in Deep Residual Networks - **
REGULARIZATION** Swapout: Learning an ensemble of deep architectures - Multi-Scale Context Aggregation by Dilated Convolutions
- Texture Synthesis Through Convolutional Neural Networks and Spectrum Constraints
- Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
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NEURAL STYLE** Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artwork - Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
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SUPERRESOLUTION** Accurate Image Super-Resolution Using Very Deep Convolutional Networks - **
HUMAN POSE ESTIMATION** Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation - **
REINFORCEMENT** Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation - **
COLORIZATION** Let there be Color
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NEURAL STYLE** Artistic Style Transfer for Videos
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REINFORCEMENT** Playing Atari with Deep Reinforcement Learning - **
GENERATIVE** Attend, Infer, Repeat: Fast Scene Understanding with Generative Models - **
ARCHITECTURES** **EFFICIENT NETWORKS** SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size - **
ACTIVATION FUNCTIONS** Noisy Activation Functions - **
OBJECT DETECTION** **IMAGE TO TEXT** DenseCap: Fully Convolutional Localization Networks for Dense Captioning
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REGULARIZATION** Deep Networks with Stochastic Depth
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GAN** Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks - **
GENERATIVE** **RNN** **ATTENTION** DRAW A Recurrent Neural Network for Image Generation - Generating Images with Perceptual Similarity Metrics based on Deep Networks
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GENERATIVE** Generative Moment Matching Networks - **
GENERATIVE** **RNN** Pixel Recurrent Neural Networks - **
GAN** Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
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NEURAL STYLE** A Neural Algorithm for Artistic Style - **
NORMALIZATION** **REGULARIZATION** Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift - **
ARCHITECTURES** Deep Residual Learning for Image Recognition - **
ACTIVATION FUNCTIONS** Fast and Accurate Deep Networks Learning By Exponential Linear Units (ELUs) - Fractional Max-Pooling
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GAN** Generative Adversarial Networks - **
ARCHITECTURES** Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning - **
NORMALIZATION** Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks