Portrait of Ross Goroshin

Ross Goroshin

Core Industry Member
Adjunct professor, Université de Montréal, Department of Computer Science and Operations Research
Google DeepMind
Research Topics
Applied AI
Computer Vision
Deep Learning
Dynamical Systems
Representation Learning

Biography

Ross Goroshin is a Research Scientist at Google DeepMind, Montreal and Core Industry Member at Mila - Quebec Artificial Intelligence Institute. He holds a PhD in Computer Science from NYU, where he was advised by Yann LeCun. He also earned a B.Eng. in Electrical Engineering from Concordia University and an M.S. in Electrical Engineering from Georgia Tech. His research focuses on computer vision, self-supervised learning, and optimal control.

In addition to his roles at Google DeepMind and Mila, Ross serves as an adjunct professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal.

Publications

An Effective Anti-Aliasing Approach for Residual Networks
Cristina Vasconcelos
Hugo Larochelle
Vincent Dumoulin
Nicolas Le Roux
Ross Goroshin
Image pre-processing in the frequency domain has traditionally played a vital role in computer vision and was even part of the standard pipe… (see more)line in the early days of deep learning. However, with the advent of large datasets, many practitioners concluded that this was unnecessary due to the belief that these priors can be learned from the data itself. Frequency aliasing is a phenomenon that may occur when sub-sampling any signal, such as an image or feature map, causing distortion in the sub-sampled output. We show that we can mitigate this effect by placing non-trainable blur filters and using smooth activation functions at key locations, particularly where networks lack the capacity to learn them. These simple architectural changes lead to substantial improvements in out-of-distribution generalization on both image classification under natural corruptions on ImageNet-C [10] and few-shot learning on Meta-Dataset [17], without introducing additional trainable parameters and using the default hyper-parameters of open source codebases.
2020-11-20
ArXiv (preprint)
arxiv.org
arxiv.org