933 citations · 6k across the 51 of their papers we have counts for
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Task-Driven Modular Networks for Zero-Shot Compositional Learning
Senthil Purushwalkam, Maximilian Nickel, Abhinav Gupta +1
One of the hallmarks of human intelligence is the ability to compose learned knowledge into novel concepts which can be recognized without a single training example. In contrast, c…
Fader Networks: Manipulating Images by Sliding Attributes
Guillaume Lample, Neil Zeghidour, Nicolas Usunier +3
This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes di…
Hard Mixtures of Experts for Large Scale Weakly Supervised Vision
Sam Gross, Marc'Aurelio Ranzato, Arthur Szlam
Training convolutional networks (CNN's) that fit on a single GPU with minibatch stochastic gradient descent has become effective in practice. However, there is still no effective m…
Web-Scale Training for Face Identification
Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato +1
Scaling machine learning methods to very large datasets has attracted considerable attention in recent years, thanks to easy access to ubiquitous sensing and data from the web. We…
On Learning Where To Look
Marc'Aurelio Ranzato
Current automatic vision systems face two major challenges: scalability and extreme variability of appearance. First, the computational time required to process an image typically…
PANDA: Pose Aligned Networks for Deep Attribute Modeling
Ning Zhang, Manohar Paluri, Marc'Aurelio Ranzato +2
We propose a method for inferring human attributes (such as gender, hair style, clothes style, expression, action) from images of people under large variation of viewpoint, pose, a…
Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition
Koray Kavukcuoglu, Marc'Aurelio Ranzato, Yann LeCun
Adaptive sparse coding methods learn a possibly overcomplete set of basis functions, such that natural image patches can be reconstructed by linearly combining a small subset of th…