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Representation Learning

329 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.

OBJECT DETECTION POSE ESTIMATION REPRESENTATION LEARNING SEMANTIC SEGMENTATION