Deep Learning Resources

Deep Learning 2016. 9. 8. 19:59

https://handong1587.github.io/deep_learning/2015/10/09/dl-resources.html

 

ImageNet

AlexNet

ImageNet Classification with Deep Convolutional Neural Networks

Network In Network

Network In Network

Batch-normalized Maxout Network in Network

GoogLeNet

Going Deeper with Convolutions

Building a deeper understanding of images

VGGNet

Very Deep Convolutional Networks for Large-Scale Image Recognition

Tensorflow VGG16 and VGG19

Inception-V2 / Inception-V3

Inception-V3 = Inception-V2 + BN-auxiliary (fully connected layer of the auxiliary classifier is also batch-normalized, not just the convolutions)

Rethinking the Inception Architecture for Computer Vision

Notes on the TensorFlow Implementation of Inception v3

https://pseudoprofound.wordpress.com/2016/08/28/notes-on-the-tensorflow-implementation-of-inception-v3/

Training an InceptionV3-based image classifier with your own dataset

ResNet

Deep Residual Learning for Image Recognition

Third-party re-implementations

https://github.com/KaimingHe/deep-residual-networks#third-party-re-implementations

Training and investigating Residual Nets

http://torch.ch/blog/2016/02/04/resnets.html

Highway Networks and Deep Residual Networks

Interpretating Deep Residual Learning Blocks as Locally Recurrent Connections

Resnet in Resnet: Generalizing Residual Architectures

ResNet-v2

Identity 89Mappings in Deep Residual Networks

Residual Networks are Exponential Ensembles of Relatively Shallow Networks

Wide Residual Networks

Deep Residual Networks for Image Classification with Python + NumPy

Residual Networks of Residual Networks: Multilevel Residual Networks

Inception-V4

Inception-V4, Inception-Resnet And The Impact Of Residual Connections On Learning

Inception-ResNet-v2

Striving for Simplicity: The All Convolutional Net

Systematic evaluation of CNN advances on the ImageNet

Deep Learning And Bayesian

Scalable Bayesian Optimization Using Deep Neural Networks

Bayesian Dark Knowledge

Memory-based Bayesian Reasoning with Deep Learning

Towards Bayesian Deep Learning: A Survey

Towards Bayesian Deep Learning: A Framework and Some Existing Methods

Autoencoders

Auto-Encoding Variational Bayes

The Potential Energy of an Autoencoder

Importance Weighted Autoencoders

Review of Auto-Encoders

Stacked What-Where Auto-encoders

Ladder Variational Autoencoders How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks

Rank Ordered Autoencoders

Decoding Stacked Denoising Autoencoders

Keras autoencoders (convolutional/fcc)

Building Autoencoders in Keras

Review of auto-encoders

Autoencoders: Torch implementations of various types of autoencoders

Tutorial on Variational Autoencoders

Variational Autoencoders Explained

Introducing Variational Autoencoders (in Prose and Code)

Under the Hood of the Variational Autoencoder (in Prose and Code)

The Unreasonable Confusion of Variational Autoencoders

Semi-Supervised Learning

Semi-Supervised Learning with Graphs (Label Propagation)

Semi-Supervised Learning with Ladder Networks

Semi-supervised Feature Transfer: The Practical Benefit of Deep Learning Today?

Unsupervised Learning

Restricted Boltzmann Machine (RBM), Sparse Coding and Auto-encoder

On Random Weights and Unsupervised Feature Learning

Unsupervised Learning of Spatiotemporally Coherent Metrics

Unsupervised Visual Representation Learning by Context Prediction

Unsupervised Learning on Neural Network Outputs

Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles

Clustering

Joint Unsupervised Learning of Deep Representations and Image Clusters

Single-Channel Multi-Speaker Separation using Deep Clustering

Deep Embedded Clustering (DEC)

Unsupervised Deep Embedding for Clustering Analysis

Transfer Learning

How transferable are features in deep neural networks?

-intro: NIPS 2014 - arxiv: http://arxiv.org/abs/1411.1792

Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks

Simultaneous Deep Transfer Across Domains and Tasks

Net2Net: Accelerating Learning via Knowledge Transfer

Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping

A theoretical framework for deep transfer learning

Transfer learning using neon

Hyperparameter Transfer Learning through Surrogate Alignment for Efficient Deep Neural Network Training

What makes ImageNet good for transfer learning?

Multi-label Learning

CNN: Single-label to Multi-label

Deep Learning for Multi-label Classification

Predicting Unseen Labels using Label Hierarchies in Large-Scale Multi-label Learning(ECML2015)

Learning with a Wasserstein Loss

From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification

CNN-RNN: A Unified Framework for Multi-label Image Classification

Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations

Multi-task Learning

Multitask Learning / Domain Adaptation

multi-task learning

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

Learning and Transferring Multi-task Deep Representation for Face Alignment

Multi-task learning of facial landmarks and expression

Heterogeneous multi-task learning for human pose estimation with deep convolutional neural network

Deep Joint Task Learning for Generic Object Extraction

Multi-Task Deep Visual-Semantic Embedding for Video Thumbnail Selection

Learning deep representation of multityped objects and tasks

Cross-stitch Networks for Multi-task Learning

Multi-Task Learning in Tensorflow (Part 1)

一箭N雕：多任务深度学习实战

Multi-modal Learning

Multimodal Deep Learning

Multimodal Convolutional Neural Networks for Matching Image and Sentence

A C++ library for Multimodal Deep Learning

Multimodal Learning for Image Captioning and Visual Question Answering

Multi modal retrieval and generation with deep distributed models

Learning Aligned Cross-Modal Representations from Weakly Aligned Data

Variational methods for Conditional Multimodal Deep Learning

Debugging Deep Learning

Some tips for debugging deep learning

Introduction to debugging neural networks

How to Visualize, Monitor and Debug Neural Network Learning

Adversarial Examples of Deep Learning

Intriguing properties of neural networks

  • arxiv: http://arxiv.org/abs/1312.6199
  • my notes: In each layer of a deep network it is the “direction” of “space” (ensemble of feature activations) which encodes useful class information rather than individual units (feature activations).

Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images

Explaining and Harnessing Adversarial Examples

Distributional Smoothing with Virtual Adversarial Training

Confusing Deep Convolution Networks by Relabelling

Exploring the Space of Adversarial Images

Learning with a Strong Adversary

Adversarial examples in the physical world

DeepFool

DeepFool: a simple and accurate method to fool deep neural networks

Adversarial Autoencoders

Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization

(Deep Learning’s Deep Flaws)’s Deep Flaws (By Zachary Chase Lipton)

Deep Learning Adversarial Examples – Clarifying Misconceptions

Adversarial Machines: Fooling A.Is (and turn everyone into a Manga)

How to trick a neural network into thinking a panda is a vulture

Deep Learning Networks

PCANet: A Simple Deep Learning Baseline for Image Classification?

Deeply-supervised Nets (DSN)

Striving for Simplicity: The All Convolutional Net

Pointer Networks

Rectified Factor Networks

FlowNet: Learning Optical Flow with Convolutional Networks

Correlational Neural Networks

Diversity Networks

A Unified Approach for Learning the Parameters of Sum-Product Networks (SPN)

Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation

Dynamic Capacity Networks

Bitwise Neural Networks

Learning Discriminative Features via Label Consistent Neural Network

A Theory of Generative ConvNet

Value Iteration Networks

How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks

Group Equivariant Convolutional Networks (G-CNNs)

Deep Spiking Networks

Low-rank passthrough neural networks

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

Deeply-Fused Nets

SNN: Stacked Neural Networks

Convolutional Neural Fabrics

Holistic SparseCNN: Forging the Trident of Accuracy, Speed, and Size

Factorized Convolutional Neural Networks

Mollifying Networks

Local Binary Convolutional Neural Networks

DenseNet

Densely Connected Convolutional Networks

CliqueCNN: Deep Unsupervised Exemplar Learning

Convexified Convolutional Neural Networks

Highway Networks

Highway Networks

Very Deep Learning with Highway Networks

Training Very Deep Networks (highway networks)

Spatial Transformer Networks

Spatial Transformer Networks

The power of Spatial Transformer Networks

Recurrent Spatial Transformer Networks

Deep Learning with Traditional Machine Learning Methods

Cascade

Compact Convolutional Neural Network Cascade for Face Detection

Bag of Words (BoW)

Deep Learning Transcends the Bag of Words

Bootstrap

Training Deep Neural Networks on Noisy Labels with Bootstrapping

Decision Tree

Neural Network and Decision Tree

Decision Forests, Convolutional Networks and the Models in-Between

SVM

Large-scale Learning with SVM and Convolutional for Generic Object Categorization

Convolutional Neural Support Vector Machines:Hybrid Visual Pattern Classifiers for Multi-robot Systems

Deep Learning using Linear Support Vector Machines

Deep Learning and Robots

Robot Learning Manipulation Action Plans by “Watching” Unconstrained Videos from the World Wide Web

Robots that can adapt like animals (Nature 2014)

End-to-End Training of Deep Visuomotor Policies

Comment on Open AI’s Efforts to Robot Learning

The Curious Robot: Learning Visual Representations via Physical Interactions

Deep Learning on Mobile Devices

Convolutional neural networks on the iPhone with VGGNet

Deep Learning in Finance

Deep Learning in Finance

A Survey of Deep Learning Techniques Applied to Trading

Applications

Some like it hot - visual guidance for preference prediction

Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey

Deep-Spying: Spying using Smartwatch and Deep Learning

Camera identification with deep convolutional networks

Build an AI Cat Chaser with Jetson TX1 and Caffe

An Analysis of Deep Neural Network Models for Practical Applications

8 Inspirational Applications of Deep Learning

16 Open Source Deep Learning Models Running as Microservices

-intro: Places 365 Classifier, Deep Face Recognition, Real Estate Classifier, Colorful Image Colorization, Illustration Tagger, InceptionNet, Parsey McParseface, ArtsyNetworks - blog: http://blog.algorithmia.com/2016/07/open-source-deep-learning-algorithm-roundup/

Makeup like a superstar: Deep Localized Makeup Transfer Network

Deep Cascaded Bi-Network for Face Hallucination

DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation

Autoencoding Blade Runner

A guy trained a machine to “watch” Blade Runner. Then things got seriously sci-fi.

http://www.vox.com/2016/6/1/11787262/blade-runner-neural-network-encoding

Deep Convolution Networks for Compression Artifacts Reduction

Deep GDashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks

Photo Filter Recommendation by Category-Aware Aesthetic Learning

  • intro: Filter Aesthetic Comparison Dataset (FACD): 28,000 filtered images and 42,240 reliable image pairs with aesthetic comparison annotations
  • arxiv: http://arxiv.org/abs/1608.05339

Instagram photos reveal predictive markers of depression

How an Algorithm Learned to Identify Depressed Individuals by Studying Their Instagram Photos

IM2CAD

Fast, Lean, and Accurate: Modeling Password Guessability Using Neural Networks

Defeating Image Obfuscation with Deep Learning

Detecting Music BPM using Neural Networks

Age Estimation

Deeply-Learned Feature for Age Estimation

Age and Gender Classification using Convolutional Neural Networks

Recurrent Face Aging

Emotion / Expression Recognition

Real-time emotion recognition for gaming using deep convolutional network features

Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns

DeXpression: Deep Convolutional Neural Network for Expression Recognition

DEX: Deep EXpectation of apparent age from a single image

How Deep Neural Networks Can Improve Emotion Recognition on Video Data

Peak-Piloted Deep Network for Facial Expression Recognition

Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution

A Recursive Framework for Expression Recognition: From Web Images to Deep Models to Game Dataset

-arxiv: http://arxiv.org/abs/1608.01647

EmotioNet

EmotioNet: EmotioNet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild

Attribution Prediction

PANDA: Pose Aligned Networks for Deep Attribute Modeling

Predicting psychological attributions from face photographs with a deep neural network

Learning Human Identity from Motion Patterns

Pose Estimation

DeepPose: Human Pose Estimation via Deep Neural Networks

Flowing ConvNets for Human Pose Estimation in Videos

Structured Feature Learning for Pose Estimation

Convolutional Pose Machines

Model-based Deep Hand Pose Estimation

Stacked Hourglass Networks for Human Pose Estimation

Chained Predictions Using Convolutional Neural Networks

DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model

Sentiment Prediction

From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction

Predict Sentiment From Movie Reviews Using Deep Learning

Neural Sentiment Classification with User and Product Attention

Place Recognition

NetVLAD: CNN architecture for weakly supervised place recognition

PlaNet - Photo Geolocation with Convolutional Neural Networks

Camera Relocalization

PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization

Modelling Uncertainty in Deep Learning for Camera Relocalization

Crowd Counting / Analysis

Large scale crowd analysis based on convolutional neural network

Cross-scene Crowd Counting via Deep Convolutional Neural Networks

Single-Image Crowd Counting via Multi-Column Convolutional Neural Network

CrowdNet

CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

Music / Sound Classification

Explaining Deep Convolutional Neural Networks on Music Classification

Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

NSFW Detection / Classification

Nipple Detection using Convolutional Neural Network

Applying deep learning to classify pornographic images and videos

MODERATE, FILTER, OR CURATE ADULT CONTENT WITH CLARIFAI’S NSFW MODEL

WHAT CONVOLUTIONAL NEURAL NETWORKS LOOK AT WHEN THEY SEE NUDITY

Image Reconstruction / Inpainting

Context Encoders: Feature Learning by Inpainting

Semantic Image Inpainting with Perceptual and Contextual Losses

Image Restoration

Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections

Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections

Image Completion with Deep Learning in TensorFlow

Image Super-Resolution

Image Super-Resolution Using Deep Convolutional Networks

Learning a Deep Convolutional Network for Image Super-Resolution

Shepard Convolutional Neural Networks


Bicubic VS. Shepard CNN

Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution

Deeply-Recursive Convolutional Network for Image Super-Resolution

Accurate Image Super-Resolution Using Very Deep Convolutional Networks

Super-Resolution with Deep Convolutional Sufficient Statistics

Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network

Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning

End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks

Accelerating the Super-Resolution Convolutional Neural Network

srez: Image super-resolution through deep learning

Image Denoising

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Medical image denoising using convolutional denoising autoencoders

Image Haze Removal

DehazeNet: An End-to-End System for Single Image Haze Removal

Blur Detection and Removal

Learning to Deblur

Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal

End-to-End Learning for Image Burst Deblurring

Image Compression

An image compression and encryption scheme based on deep learning

Full Resolution Image Compression with Recurrent Neural Networks

Depth Prediction

Deeper Depth Prediction with Fully Convolutional Residual Networks

Texture Synthesis

Texture Synthesis Using Convolutional Neural Networks

Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis

Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

Generative Adversarial Text to Image Synthesis

Image Tagging

Flexible Image Tagging with Fast0Tag

Music Tagging

Automatic tagging using deep convolutional neural networks

Benchmarks

Deep Learning’s Accuracy

Benchmarks for popular CNN models

https://github.com/jcjohnson/cnn-benchmarks

Papers

Reweighted Wake-Sleep

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

Deeply-Supervised Nets

Deep learning

On the Expressive Power of Deep Learning: A Tensor Analysis

Understanding and Predicting Image Memorability at a Large Scale

Towards Open Set Deep Networks

Structured Prediction Energy Networks (SPEN)

A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction

Deep Neural Networks predict Hierarchical Spatio-temporal Cortical Dynamics of Human Visual Object Recognition

A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction

Understanding Deep Convolutional Networks

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Exploiting Cyclic Symmetry in Convolutional Neural Networks

Cross-dimensional Weighting for Aggregated Deep Convolutional Features

Understanding Visual Concepts with Continuation Learning

Learning Efficient Algorithms with Hierarchical Attentive Memory

DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks

Do Deep Convolutional Nets Really Need to be Deep (Or Even Convolutional)?

Harnessing Deep Neural Networks with Logic Rules

Degrees of Freedom in Deep Neural Networks

Deep Networks with Stochastic Depth

LIFT: Learned Invariant Feature Transform

Understanding How Image Quality Affects Deep Neural Networks

Deep Embedding for Spatial Role Labeling

Learning Convolutional Neural Networks for Graphs

Unreasonable Effectiveness of Learning Neural Nets: Accessible States and Robust Ensembles

Learning Deep Representation for Imbalanced Classification

FractalNet

FractalNet: Ultra-Deep Neural Networks without Residuals

Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images

Convolutional Neural Networks Analyzed via Convolutional Sparse Coding

Recent Advances in Convolutional Neural Networks

TI-POOLING: transformation-invariant pooling for feature learning in Convolutional Neural Networks

Why does deep and cheap learning work so well?

STDP

A biological gradient descent for prediction through a combination of STDP and homeostatic plasticity

An objective function for STDP

Towards a Biologically Plausible Backprop

Target Propagation

How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target Propagation

Difference Target Propagation

CNN with Computer Vision

End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression

A convnet for non-maximum suppression

A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

Projects

Top Deep Learning Projects

deepnet: Implementation of some deep learning algorithms

DeepNeuralClassifier(Julia): Deep neural network using rectified linear units to classify hand written digits from the MNIST dataset

Clarifai Node.js Demo

Visual Search Server

Deep Learning in Rust

Implementation of state-of-art models in Torch

Deep Learning (Python, C, C++, Java, Scala, Go)

deepmark: THE Deep Learning Benchmarks

Siamese Net

  • intro: “This package shows how to train a siamese network using Lasagne and Theano and includes network definitions for state-of-the-art networks including: DeepID, DeepID2, Chopra et. al, and Hani et. al. We also include one pre-trained model using a custom convolutional network.”
  • github: https://github.com/Kadenze/siamese_net

PRE-TRAINED CONVNETS AND OBJECT LOCALISATION IN KERAS

Deep Learning algorithms with TensorFlow: Ready to use implementations of various Deep Learning algorithms using TensorFlow

Fast Multi-threaded VGG 19 Feature Extractor

Live demo of neural network classifying images

http://ml4a.github.io/dev/demos/cifar_confusion.html#

mojo cnn: c++ convolutional neural network

DeepHeart: Neural networks for monitoring cardiac data

Deep Water: Deep Learning in H2O using Native GPU Backends

Greentea LibDNN: Greentea LibDNN - a universal convolution implementation supporting CUDA and OpenCL

Dracula: A spookily good Part of Speech Tagger optimized for Twitter

Trained image classification models for Keras

PyCNN: Cellular Neural Networks Image Processing Python Library

regl-cnn: Digit recognition with Convolutional Neural Networks in WebGL

gvnn

gvnn: Neural Network Library for Geometric Computer Vision

Readings and Questions

What you wanted to know about AI

http://fastml.com/what-you-wanted-to-know-about-ai/

Epoch vs iteration when training neural networks

Questions to Ask When Applying Deep Learning

http://deeplearning4j.org/questions.html

How can I know if Deep Learning works better for a specific problem than SVM or random forest?

What is the difference between deep learning and usual machine learning?

Resources

Awesome Deep Learning

Awesome-deep-vision: A curated list of deep learning resources for computer vision

Applied Deep Learning Resources: A collection of research articles, blog posts, slides and code snippets about deep learning in applied settings.

Deep Learning Libraries by Language

Deep Learning Resources

http://yanirseroussi.com/deep-learning-resources/

Deep Learning Resources

https://omtcyfz.github.io/2016/08/29/Deep-Learning-Resources.html

Turing Machine: musings on theory & code(DEEP LEARNING REVOLUTION, summer 2015, state of the art & topnotch links)

https://vzn1.wordpress.com/2015/09/01/deep-learning-revolution-summer-2015-state-of-the-art-topnotch-links/

BICV Group: Biologically Inspired Computer Vision research group

http://www.bicv.org/deep-learning/

Learning Deep Learning

http://rt.dgyblog.com/ref/ref-learning-deep-learning.html

Summaries and notes on Deep Learning research papers

Deep Learning Glossary

The Deep Learning Playbook

https://medium.com/@jiefeng/deep-learning-playbook-c5ebe34f8a1a#.eg9cdz5ak

Deep Learning Study: Study of HeXA@UNIST in Preparation for Submission

Deep Learning Books

awesome-very-deep-learning: A curated list of papers and code about very deep neural networks (50+ layers)

Deep Learning Resources and Tutorials using Keras and Lasagne

Deep Learning: Definition, Resources, Comparison with Machine Learning

Awesome - Most Cited Deep Learning Papers

The most cited papers in computer vision and deep learning

deep learning papers: A place to collect papers that are related to deep learning and computational biology

papers-I-read

LEARNING DEEP LEARNING - MY TOP-FIVE LIST

Attention

Tools

DNNGraph - A deep neural network model generation DSL in Haskell

Deep playground: an interactive visualization of neural networks, written in typescript using d3.js

Neural Network Package

  • intro: This package provides an easy and modular way to build and train simple or complex neural networks using Torch
  • github: https://github.com/torch/nn

deepdish: Deep learning and data science tools from the University of Chicago deepdish: Serving Up Chicago-Style Deep Learning

AETROS CLI: Console application to manage deep neural network training in AETROS Trainer

Books

Deep Learning

Fundamentals of Deep Learning: Designing Next-Generation Artificial Intelligence Algorithms

FIRST CONTACT WITH TENSORFLOW: Get started with with Deep Learning programming

Make Your Own Neural Network: IPython Neural Networks on a Raspberry Pi Zero

Blogs

Neural Networks and Deep Learning

http://neuralnetworksanddeeplearning.com

Deep Learning Reading List

http://deeplearning.net/reading-list/

WILDML: A BLOG ABOUT MACHINE LEARNING, DEEP LEARNING AND NLP.

http://www.wildml.com/

Andrej Karpathy blog

http://karpathy.github.io/

Rodrigob’s github page

http://rodrigob.github.io/

colah’s blog

http://colah.github.io/

What My Deep Model Doesn’t Know…

http://mlg.eng.cam.ac.uk/yarin/blog_3d801aa532c1ce.html

Christoph Feichtenhofer

 

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