Browse SoTA > Methodology > Representation Learning > Unsupervised Representation Learning

Unsupervised Representation Learning

43 papers with code · Methodology

Leaderboards

You can find evaluation results in the subtasks. You can also submitting evaluation metrics for this task.

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

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 PART-OF-SPEECH TAGGING UNSUPERVISED REPRESENTATION LEARNING

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

TabNet: Attentive Interpretable Tabular Learning

ICLR 2020 google-research/google-research

We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet.

DECISION MAKING FEATURE SELECTION SELF-SUPERVISED LEARNING UNSUPERVISED REPRESENTATION LEARNING

Continual Unsupervised Representation Learning

NeurIPS 2019 deepmind/deepmind-research

Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially.

CONTINUAL LEARNING OMNIGLOT UNSUPERVISED REPRESENTATION LEARNING

Continual Unsupervised Representation Learning

NeurIPS 2019 deepmind/deepmind-research

Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially.

CONTINUAL LEARNING OMNIGLOT UNSUPERVISED REPRESENTATION LEARNING

Visual Reinforcement Learning with Imagined Goals

NeurIPS 2018 vitchyr/rlkit

For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires.

UNSUPERVISED REPRESENTATION LEARNING

Generative Pretraining from Pixels

Preprint 2020 openai/image-gpt

Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images.

SELF-SUPERVISED IMAGE CLASSIFICATION UNSUPERVISED REPRESENTATION LEARNING

Unsupervised Representation Learning by Predicting Image Rotations

ICLR 2018 gidariss/FeatureLearningRotNet

However, in order to successfully learn those features, they usually require massive amounts of manually labeled data, which is both expensive and impractical to scale.

UNSUPERVISED REPRESENTATION LEARNING