Browse SoTA > Methodology > Representation Learning > Unsupervised Representation Learning

Unsupervised Representation Learning

64 papers with code · Methodology

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Greatest papers with code

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

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

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 IMAGE CLUSTERING UNSUPERVISED REPRESENTATION LEARNING

TabNet: Attentive Interpretable Tabular Learning

20 Aug 2019google-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

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

Viewmaker Networks: Learning Views for Unsupervised Representation Learning

ICLR 2021 makcedward/nlpaug

However, designing these views requires considerable human expertise and experimentation, hindering widespread adoption of unsupervised representation learning methods across domains and modalities.

CONTRASTIVE LEARNING UNSUPERVISED REPRESENTATION LEARNING

Generative Pretraining from Pixels

ICML 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.

Ranked #11 on Image Classification on STL-10 (using extra training data)

SELF-SUPERVISED IMAGE CLASSIFICATION UNSUPERVISED REPRESENTATION LEARNING

Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models

ACL 2020 google-research/language

Recent models for unsupervised representation learning of text have employed a number of techniques to improve contextual word representations but have put little focus on discourse-level representations.

COMMON SENSE REASONING NATURAL LANGUAGE INFERENCE READING COMPREHENSION UNSUPERVISED REPRESENTATION LEARNING