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This repository contains short summaries of some machine learning papers.

Added 2018/10/01:

• *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

Added 2018/03/08:

• *ARCHITECTURES ATTENTION* Spatial Transformer Networks (thanks, alexobednikov)

Added 2018/03/06:

• *LOSS FUNCTIONS RECOGNITION* Working hard to know your neighbor’s margins: Local descriptor learning loss (thanks, alexobednikov)

Added 2017/12/13:

• *FACE RECOGNITION FACES* Neural Aggregation Network for Video Face Recognition (thanks, alexobednikov)

Added 2017/12/03:

• Critical Learning Periods in Deep Neural Networks


• *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

Added 2017/10/28:

• *GAN* Progressive Growing of GANs for Improved Quality, Stability, and Variation

Added 2017/10/24:

• *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

Added 2017/10/21:

• Snapshot Ensembles: Train 1, get M for free


• Image Crowd Counting Using Convolutional Neural Network and Markov Random Field


• *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

Added 2017/09/24:

• *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

Added 2017/08/08:

• *FACES* Multi-view Face Detection Using Deep Convolutional Neural Networks (aka DDFD) (thanks, arnaldog12)

Added 2017/06/11:

• *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)

Added 2017/03/15:

• *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

Added 2017/03/14:

• *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

Added 2016/07/29:

• *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

Added 2016/07/01:

• *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


• *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

Added 2016/06/06:

• *ARCHITECTURES* FractalNet: Ultra-Deep Neural Networks without Residuals


• PlaNet - Photo Geolocation with Convolutional Neural Networks


• *OPTIMIZERS* Adam: A Method for Stochastic Optimization


• *GAN RNN* Generating images with recurrent adversarial networks


• *GAN* Adversarially Learned Inference

Added 2016/06/02:

• *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

Added 2016/05/15:

• *NEURAL STYLE* Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artwork


• Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis


• *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

Added 2016/05/08:

• *NEURAL STYLE* Artistic Style Transfer for Videos

Added 2016/05/03:

• *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

Added 2016/04/01:

• *REGULARIZATION* Deep Networks with Stochastic Depth

Added 2016/03/31:

• *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


• *GENERATIVE* Generative Moment Matching Networks


• *GENERATIVE RNN* Pixel Recurrent Neural Networks


• *GAN* Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Added 2016/03/??:

• *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


• *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