Search Results for author: Ross Goroshin

Found 11 papers, 5 papers with code

Impact of Aliasing on Generalization in Deep Convolutional Networks

no code implementations ICCV 2021 Cristina Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Rob Romijnders, Nicolas Le Roux, Ross Goroshin

We investigate the impact of aliasing on generalization in Deep Convolutional Networks and show that data augmentation schemes alone are unable to prevent it due to structural limitations in widely used architectures.

Data Augmentation Few-Shot Learning +1

Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark

1 code implementation6 Apr 2021 Vincent Dumoulin, Neil Houlsby, Utku Evci, Xiaohua Zhai, Ross Goroshin, Sylvain Gelly, Hugo Larochelle

To bridge this gap, we perform a cross-family study of the best transfer and meta learners on both a large-scale meta-learning benchmark (Meta-Dataset, MD), and a transfer learning benchmark (Visual Task Adaptation Benchmark, VTAB).

Few-Shot Learning General Classification +1

An Effective Anti-Aliasing Approach for Residual Networks

no code implementations20 Nov 2020 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 pipeline in the early days of deep learning.

Few-Shot Learning Image Classification

An Analysis of Object Representations in Deep Visual Trackers

no code implementations8 Jan 2020 Ross Goroshin, Jonathan Tompson, Debidatta Dwibedi

Despite these strong priors, we show that deep trackers often default to tracking by saliency detection - without relying on the object instance representation.

Saliency Detection Visual Tracking

Learning to Linearize Under Uncertainty

no code implementations NeurIPS 2015 Ross Goroshin, Michael Mathieu, Yann Lecun

Training deep feature hierarchies to solve supervised learning tasks has achieved state of the art performance on many problems in computer vision.

Stacked What-Where Auto-encoders

2 code implementations8 Jun 2015 Junbo Zhao, Michael Mathieu, Ross Goroshin, Yann Lecun

The objective function includes reconstruction terms that induce the hidden states in the Deconvnet to be similar to those of the Convnet.

Semi-Supervised Image Classification

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