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papers & summaries & ideas on Machine Learning

Recent Readings

2020

• 01/16/2020: Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marder Discovery
• 01/23/2020: A Survey on GANs for Anomaly Detection
• 01/30/2020: Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Network
• 02/06/2020: Adversarial Feature Learning
• 02/13/2020: Adversarially Learned Inference
• 02/20/2020: Extracting and Composing Robust Features with Denoising Autoencoders
• 02/27/2020: Efficient GAN-Based Anomaly Detection
• 03/05/2020: GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
  Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection
• 03/19/2020: Adversarially learned anomaly detection
• 03/26/2020: DOPING: Generative data augmentation for unsupervised anomaly detection with GAN
• 04/02/2020: Generative probabilistic novelty detection with adversarial autoencoders
• 04/09/2020: Adversarially learned one-class classifier for novelty detection
• 04/16/2020: Fence GAN: towards better anomaly detection
• 04/23/2020: Generative Adversarial Active Learning for Unsupervised Outlier Detection
• 04/30/2020: Novelty Detection with GAN
• 05/07/2020: OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations
• 07/21/2020: Learning Discriminative Reconstructions for Unsupervised Outlier Removal
• 07/28/2020: Unsupervised representation learning by predicting image rotations
• 08/04/2020: A Simple Framework for Contrastive Learning of Visual Representations
• 08/18/2020: Self-Supervised Representation Learning by Rotation Feature Decoupling
• 08/27/2020: Classification-Based Anomaly Detection for General Data
• 09/03/2020: Generative-discriminative Feature Representations for Open-set Recognition
• 09/10/2020: [Deep Clustering for Unsupervised Learning of Visual Features(https://openaccess.thecvf.com/content_ECCV_2018/papers/Mathilde_Caron_Deep_Clustering_for_ECCV_2018_paper.pdf)
• 09/17/2020: Self-labelling via simultaneously clustering and representation learning
• 09/24/2020: Automatically Discovering and Learning New Visual Categories with Ranking Statistics
• 10/01/2020: Unsupervised Clustering using Pseudo-semi-supervised Learning
• 10/08/2020: Multi-class classification without multiclass labels
• 10/15/2020: Learning to Discover Novel Visual Categories via Deep Transfer Clustering
• 10/22/2020: Unsupervised deep embedding for clustering analysis
• 10/29/2020: Deep Adaptive Image Clustering
• 11/05/2020: Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization
• 11/12/2020: Invariant Information Clustering for Unsupervised Image Classification and Segmentation
• 11/19/2020: Learning discrete representations via information maximizing self augmented training

2021

• 01/14/2021: Deep Continuous Clustering
• 01/21/2021: Associative Deep Clustering: Training a Classification Network with No Labels
• 01/28/2021: Self-Supervised Relational Reasoning for Representation Learning
• 02/04/2021: Automatic Shortcut Removal for Self-Supervised Representation Learning
• 02/11/2021: Big Self-Supervised Models are Strong Semi-Supervised Learners
• 02/18/2021: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
• 02/25/2021: Deep Transformation-Invariant Clustering
• 03/04/2021: Self-Supervised Learning for Generalizable Out-of-Distribution Detection
• 03/11/2021: SelectiveNet: A Deep Neural Network with an Integrated Reject Option
• 03/18/2021: Multi-class Data Description for Out-of-distribution Detection
• 03/25/2021: Conditional Gaussian Distribution Learning for Open Set Recognition
• 04/01/2021: Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data
• 04/08/2021: Few-Shot Open-Set Recognition using Meta-Learning
• 04/15/2021: Self-Supervised Learning of Pretext-Invariant Representations
• 04/22/2021: How Useful Is Self-Supervised Pretraining for Visual Tasks?
• 04/29/2021: Online Deep Clustering for Unsupervised Representation Learning
• 05/06/2021: Convolutional Neural Networks with Compression Complexity Pooling for Out-of-Distribution Image Detection
• 05/13/2021: Unsupervised Representation Learning by Predicting Random Distances
• 08/26/2021: C2ae: Class conditioned auto-encoder for open-set recognition
• 09/09/2021: Classification-reconstruction learning for open-set recognition

Week 1

• Deep Learning
• Learning a Neural-network-based Representation for Open Set Recognition

Week 2

• “Why Should I Trust You?” Explaining the Predictions of Any Classifier
• Towards Open Set Deep Networks

WEEK 3

• A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation
• Distributed Representations of Words and Phrases and their Compositionality

Week 4

• Learning to Forget: Continual Prediction with LSTM
• Generative Adversarial Nets

Week 5

• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

Week 6

• Glove: Global Vectors for Word Representation
• Effective Approaches to Attention-based Neural Machine Translation

Week 7

• Adversarial Autoencoder
• Look and Think Twice: Capturing Top-Down Visual Attention with Feedback Convolutional Neural Networks

Week 8

• Generative OpenMax for Multi-Class Open Set Classification
• Neural Machine Translation by Jointly Learning to Align and Translate

Week 9

• Open Category Classification by Adversarial Sample Generation
• Large-Scale Evolution of Image Classifiers

Week 10

• Open Set Learning with Counterfactual Images
• Optimization as a Model For Few-shot Learning

Week 11

• Nearest Neighbors Distance Ratio Open-set Classifier
• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Week 12

• Open Set Domain Adaptation by Backpropagation
• Conditional generative adversarial nets

Week 13

• Unseen Class Discovery in Open-world Classification
• Unsupervised representation learning with deep convolutional generative adversarial networks

Week 14

• Open Set Recognition by Regularising Classifier with Fake Data Generated by Generative Adversarial Networks
• A Discriminative Feature Learning Approach for Deep Face Recognition

Week 15

• Distribution Networks for Open Set Learning
• Triplet-Center Loss for Multi-View 3D Object Retrieval

Week 16

• ODN: Opening the Deep Network for Open-Set Action Recognition
• ArcFace: Additive Angular Margin Loss for Deep Face Recognition

Week 2

• Co-Representation Learning for Classification and Novel Class Detection via Deep Networks

Week 3

• Deep Anomaly Detection with Outlier Exposure

Week 4

• Reducing Network Agnostophobia

Week 5

• Learning Deep Features for One-Class Classification

Week 1

• Semi-supervised classification with graph convolutional networks
• Large-scale malware classification using random projections and neural networks

Week 2

• Line: Large-scale information network embedding
• Representation learning on graphs with jumping knowledge networks