Unsupervised Representation Learning

120 papers with code • 0 benchmarks • 2 datasets

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Greatest papers with code

Semi-Supervised Sequence Modeling with Cross-View Training

tensorflow/models EMNLP 2018

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 +6

Meta-Learning Update Rules for Unsupervised Representation Learning

tensorflow/models ICLR 2019

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

tensorflow/models 19 Nov 2015

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

Conditional Image Generation Image Clustering +1

TabNet: Attentive Interpretable Tabular Learning

google-research/google-research 20 Aug 2019

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

Decision Making Feature Selection +3

Learning and Evaluating Contextual Embedding of Source Code

google-research/google-research ICML 2020

We fine-tune CuBERT on our benchmark tasks, and compare the resulting models to different variants of Word2Vec token embeddings, BiLSTM and Transformer models, as well as published state-of-the-art models, showing that CuBERT outperforms them all, even with shorter training, and with fewer labeled examples.

Contextual Embedding for Source Code Exception type +7

Continual Unsupervised Representation Learning

deepmind/deepmind-research NeurIPS 2019

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 Unsupervised Representation Learning

Unsupervised Representation Learning by Predicting Image Rotations

facebookresearch/vissl ICLR 2018

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.

General Classification Self-Supervised Image Classification +1

Visual Reinforcement Learning with Imagined Goals

vitchyr/rlkit NeurIPS 2018

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

openai/image-gpt ICML 2020

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)

Fine-tuning Self-Supervised Image Classification +1