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Representation Learning

306 papers with code · Methodology

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

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.

META-LEARNING UNSUPERVISED REPRESENTATION LEARNING

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.

IMAGE GENERATION REPRESENTATION LEARNING UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

19 Nov 2015tensorflow/models

In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.

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.

CCG SUPERTAGGING DEPENDENCY PARSING MACHINE TRANSLATION MULTI-TASK LEARNING NAMED ENTITY RECOGNITION UNSUPERVISED REPRESENTATION LEARNING

Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks

9 Apr 2019rusty1s/pytorch_geometric

We propose a dynamic neighborhood aggregation (DNA) procedure guided by (multi-head) attention for representation learning on graphs.

NODE CLASSIFICATION REPRESENTATION LEARNING

Fast Graph Representation Learning with PyTorch Geometric

6 Mar 2019rusty1s/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.

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION RELATIONAL REASONING

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.

ANOMALY DETECTION REPRESENTATION LEARNING VIDEO ALIGNMENT

Deep High-Resolution Representation Learning for Visual Recognition

20 Aug 2019CSAILVision/semantic-segmentation-pytorch

High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection.

 SOTA for Semantic Segmentation on Cityscapes (using extra training data)

OBJECT DETECTION POSE ESTIMATION REPRESENTATION LEARNING SEMANTIC SEGMENTATION