Representation Learning
Representation learning is concerned with training machine learning algorithms to learn useful representations, e.g. those that are interpretable, have latent features, or can be used for transfer learning.
( Image credit: Visualizing and Understanding Convolutional Networks )
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|
Subtasks
Greatest papers with code
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
ICLR 2019
• tensorflow/models
• 
We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning.
CONTINUOUS CONTROL HIERARCHICAL REINFORCEMENT LEARNING REPRESENTATION LEARNING
Semi-Supervised Sequence Modeling with Cross-View Training
EMNLP 2018
• tensorflow/models
•
We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data.
SOTA for CCG Supertagging on CCGBank
CCG SUPERTAGGING DEPENDENCY PARSING MACHINE TRANSLATION MULTI-TASK LEARNING NAMED ENTITY RECOGNITION UNSUPERVISED REPRESENTATION LEARNING
Meta-Learning Update Rules for Unsupervised Representation Learning
ICLR 2019
• tensorflow/models
•
Specifically, we target semi-supervised classification performance, and we meta-learn an algorithm -- an unsupervised weight update rule -- that produces representations useful for this task.
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
NeurIPS 2016
• tensorflow/models
•
This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner.
#5 best model for
Unsupervised MNIST
on MNIST
IMAGE GENERATION REPRESENTATION LEARNING UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.
#9 best model for
Conditional Image Generation
on CIFAR-10
CONDITIONAL IMAGE GENERATION UNSUPERVISED REPRESENTATION LEARNING
Temporal Cycle-Consistency Learning
CVPR 2019
• google-research/google-research
•
We introduce a self-supervised representation learning method based on the task of temporal alignment between videos.
Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks
We propose a dynamic neighborhood aggregation (DNA) procedure guided by (multi-head) attention for representation learning on graphs.
#6 best model for
Node Classification
on Citeseer
Fast Graph Representation Learning with PyTorch Geometric
We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.
#2 best model for
Graph Classification
on REDDIT-B
GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION RELATIONAL REASONING
Deep High-Resolution Representation Learning for Visual Recognition
• CSAILVision/semantic-segmentation-pytorch
• 
High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection.
#3 best model for
Semantic Segmentation
on PASCAL Context
OBJECT DETECTION POSE ESTIMATION REPRESENTATION LEARNING SEMANTIC SEGMENTATION
High-Resolution Representations for Labeling Pixels and Regions
• CSAILVision/semantic-segmentation-pytorch
•
The proposed approach achieves superior results to existing single-model networks on COCO object detection.
#3 best model for
Semantic Segmentation
on LIP val
FACIAL LANDMARK DETECTION OBJECT DETECTION POSE ESTIMATION REPRESENTATION LEARNING SEMANTIC SEGMENTATION