4 citations · 7 across the 2 of their papers we have counts for
6 papers
Neighbourhood Distillation: On the benefits of non end-to-end distillation
Laëtitia Shao, Max Moroz, Elad Eban +1
End-to-end training with back propagation is the standard method for training deep neural networks. However, as networks become deeper and bigger, end-to-end training becomes more…
Fine-Grained Stochastic Architecture Search
Shraman Ray Chaudhuri, Elad Eban, Hanhan Li +2
State-of-the-art deep networks are often too large to deploy on mobile devices and embedded systems. Mobile neural architecture search (NAS) methods automate the design of small mo…
Sky Optimization: Semantically aware image processing of skies in low-light photography
Orly Liba, Longqi Cai, Yun-Ta Tsai +5
The sky is a major component of the appearance of a photograph, and its color and tone can strongly influence the mood of a picture. In nighttime photography, the sky can also suff…
Structured Multi-Hashing for Model Compression
Elad Eban, Yair Movshovitz-Attias, Hao Wu +4
Despite the success of deep neural networks (DNNs), state-of-the-art models are too large to deploy on low-resource devices or common server configurations in which multiple models…
Synthetic Depth-of-Field with a Single-Camera Mobile Phone
Neal Wadhwa, Rahul Garg, David E. Jacobs +7
Shallow depth-of-field is commonly used by photographers to isolate a subject from a distracting background. However, standard cell phone cameras cannot produce such images optical…
No Fuss Distance Metric Learning using Proxies
Yair Movshovitz-Attias, Alexander Toshev, Thomas K. Leung +2
We address the problem of distance metric learning (DML), defined as learning a distance consistent with a notion of semantic similarity. Traditionally, for this problem supervisio…