2.2k citations · 10.8k across the 93 of their papers we have counts for
13 papers · 1 filter
Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision
Xinchen Yan, Jimei Yang, Ersin Yumer +2
Understanding the 3D world is a fundamental problem in computer vision. However, learning a good representation of 3D objects is still an open problem due to the high dimensionalit…
Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents
Rui Zhang, Honglak Lee, Dragomir Radev
The goal of sentence and document modeling is to accurately represent the meaning of sentences and documents for various Natural Language Processing tasks. In this work, we present…
Sentence Ordering and Coherence Modeling using Recurrent Neural Networks
Lajanugen Logeswaran, Honglak Lee, Dragomir Radev
Modeling the structure of coherent texts is a key NLP problem. The task of coherently organizing a given set of sentences has been commonly used to build and evaluate models that u…
Learning What and Where to Draw
Scott Reed, Zeynep Akata, Santosh Mohan +3
Generative Adversarial Networks (GANs) have recently demonstrated the capability to synthesize compelling real-world images, such as room interiors, album covers, manga, faces, bir…
Deep Variational Canonical Correlation Analysis
Weiran Wang, Xinchen Yan, Honglak Lee +1
We present deep variational canonical correlation analysis (VCCA), a deep multi-view learning model that extends the latent variable model interpretation of linear CCA to nonlinear…
Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification
Yuting Zhang, Kibok Lee, Honglak Lee
Unsupervised learning and supervised learning are key research topics in deep learning. However, as high-capacity supervised neural networks trained with a large amount of labels h…
Generative Adversarial Text to Image Synthesis
Scott Reed, Zeynep Akata, Xinchen Yan +3
Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. However, in recent years generic and powerfu…
Control of Memory, Active Perception, and Action in Minecraft
Junhyuk Oh, Valliappa Chockalingam, Satinder Singh +1
In this paper, we introduce a new set of reinforcement learning (RL) tasks in Minecraft (a flexible 3D world). We then use these tasks to systematically compare and contrast existi…
Learning Deep Representations of Fine-grained Visual Descriptions
Scott Reed, Zeynep Akata, Bernt Schiele +1
State-of-the-art methods for zero-shot visual recognition formulate learning as a joint embedding problem of images and side information. In these formulations the current best com…
Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games
Xiaoxiao Guo, Satinder Singh, Richard Lewis +1
Monte Carlo Tree Search (MCTS) methods have proven powerful in planning for sequential decision-making problems such as Go and video games, but their performance can be poor when t…
Object Contour Detection with a Fully Convolutional Encoder-Decoder Network
Jimei Yang, Brian Price, Scott Cohen +2
We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Different from previous low-level edge detection, our algorithm focus…
Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units
Wenling Shang, Kihyuk Sohn, Diogo Almeida +1
Recently, convolutional neural networks (CNNs) have been used as a powerful tool to solve many problems of machine learning and computer vision. In this paper, we aim to provide in…
Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis
Jimei Yang, Scott Reed, Ming-Hsuan Yang +1
An important problem for both graphics and vision is to synthesize novel views of a 3D object from a single image. This is particularly challenging due to the partial observability…