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.
We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning.
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.
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.
#5 best model for Unsupervised MNIST on MNIST
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.
#6 best model for Conditional Image Generation on CIFAR-10
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.
SOTA for CCG Supertagging on CCGBank
We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.
In this work, we propose to train CNNs from images annotated with multiple tags, to enhance the quality of visual representation of the trained CNN model.
What will happen if we increase the dataset size by 10x or 100x?
#12 best model for Semantic Segmentation on PASCAL VOC 2012
Sentence vectors represent an appealing approach to meaning: learn an embedding that encompasses the meaning of a sentence in a single vector, that can be used for a variety of semantic tasks.