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.
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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
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.
#6 best model for
Conditional Image Generation
on CIFAR-10
CONDITIONAL IMAGE GENERATION UNSUPERVISED 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 (NER) UNSUPERVISED REPRESENTATION LEARNING
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.
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.
Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning
In this work, we propose to train CNNs from images annotated with multiple tags, to enhance the quality of visual representation of the trained CNN model.
IMAGE CLASSIFICATION OBJECT DETECTION REPRESENTATION LEARNING SEMANTIC SEGMENTATION TRANSFER LEARNING
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
ICCV 2017
• Tencent/tencent-ml-images
•
What will happen if we increase the dataset size by 10x or 100x?
#12 best model for
Semantic Segmentation
on PASCAL VOC 2012
IMAGE CLASSIFICATION OBJECT DETECTION POSE ESTIMATION REPRESENTATION LEARNING SEMANTIC SEGMENTATION
DisSent: Sentence Representation Learning from Explicit Discourse Relations
• facebookresearch/InferSent
• 
Sentence vectors represent an appealing approach to meaning: learn an embedding that encompasses the meaning of a sentence in a single vector, that can be used for a variety of semantic tasks.