Puneet K. Dokania

research

∙ 09/24/2022

Raising the Bar on the Evaluation of Out-of-Distribution Detection

In image classification, a lot of development has happened in detecting ...

Jishnu Mukhoti, et al. ∙

research

∙ 07/22/2022

An Impartial Take to the CNN vs Transformer Robustness Contest

Following the surge of popularity of Transformers in Computer Vision, se...

Francesco Pinto, et al. ∙

research

∙ 07/13/2022

Sample-dependent Adaptive Temperature Scaling for Improved Calibration

It is now well known that neural networks can be wrong with high confide...

Tom Joy, et al. ∙

research

∙ 06/29/2022

RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness

We show that the effectiveness of the well celebrated Mixup [Zhang et al...

Francesco Pinto, et al. ∙

research

∙ 06/16/2022

Catastrophic overfitting is a bug but also a feature

Despite clear computational advantages in building robust neural network...

Guillermo Ortiz-Jiménez, et al. ∙

research

∙ 02/02/2022

Make Some Noise: Reliable and Efficient Single-Step Adversarial Training

Recently, Wong et al. showed that adversarial training with single-step ...

Pau de Jorge, et al. ∙

research

∙ 07/09/2021

ANCER: Anisotropic Certification via Sample-wise Volume Maximization

Randomized smoothing has recently emerged as an effective tool that enab...

Francisco Eiras, et al. ∙

research

∙ 04/01/2021

No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks

There has been increasing interest in building deep hierarchy-aware clas...

Shyamgopal Karthik, et al. ∙

research

∙ 12/24/2020

On Batch Normalisation for Approximate Bayesian Inference

We study batch normalisation in the context of variational inference met...

Jishnu Mukhoti, et al. ∙

research

∙ 10/22/2020

Continual Learning in Low-rank Orthogonal Subspaces

In continual learning (CL), a learner is faced with a sequence of tasks,...

Arslan Chaudhry, et al. ∙

research

∙ 10/10/2020

Diagnosing and Preventing Instabilities in Recurrent Video Processing

Recurrent models are becoming a popular choice for video enhancement tas...

Thomas Tanay, et al. ∙

research

∙ 07/08/2020

How benign is benign overfitting?

We investigate two causes for adversarial vulnerability in deep neural n...

Amartya Sanyal, et al. ∙

research

∙ 06/16/2020

Progressive Skeletonization: Trimming more fat from a network at initialization

Recent studies have shown that skeletonization (pruning parameters) of n...

Pau de Jorge, et al. ∙

research

∙ 04/20/2020

A Revised Generative Evaluation of Visual Dialogue

Evaluating Visual Dialogue, the task of answering a sequence of question...

Daniela Massiceti, et al. ∙

research

∙ 02/21/2020

Calibrating Deep Neural Networks using Focal Loss

Miscalibration – a mismatch between a model's confidence and its correct...

Jishnu Mukhoti, et al. ∙

research

∙ 02/19/2020

Using Hindsight to Anchor Past Knowledge in Continual Learning

In continual learning, the learner faces a stream of data whose distribu...

Arslan Chaudhry, et al. ∙

research

∙ 10/18/2019

Mirror Descent View for Neural Network Quantization

Quantizing large Neural Networks (NN) while maintaining the performance ...

Thalaiyasingam Ajanthan, et al. ∙

research

∙ 09/24/2019

Interactive Sketch & Fill: Multiclass Sketch-to-Image Translation

Arnab Ghosh 6:32 PM We propose an interactive GAN-based sketch-to-image ...

Arnab Ghosh, et al. ∙

research

∙ 06/11/2019

Stable Rank Normalization for Improved Generalization in Neural Networks and GANs

Exciting new work on the generalization bounds for neural networks (NN) ...

Amartya Sanyal, et al. ∙

research

∙ 02/27/2019

Continual Learning with Tiny Episodic Memories

Learning with less supervision is a major challenge in artificial intell...

Arslan Chaudhry, et al. ∙

research

∙ 12/16/2018

Visual Dialogue without Vision or Dialogue

We characterise some of the quirks and shortcomings in the exploration o...

Daniela Massiceti, et al. ∙

research

∙ 12/11/2018

Proximal Mean-field for Neural Network Quantization

Compressing large neural networks by quantizing the parameters, while ma...

Thalaiyasingam Ajanthan, et al. ∙

research

∙ 09/24/2018

Weakly-Supervised Learning of Metric Aggregations for Deformable Image Registration

Deformable registration has been one of the pillars of biomedical image ...

Enzo Ferrante, et al. ∙

research

∙ 01/30/2018

Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence

We study the incremental learning problem for the classification task, a...

Arslan Chaudhry, et al. ∙

research

∙ 07/19/2017

Deformable Registration through Learning of Context-Specific Metric Aggregation

We propose a novel weakly supervised discriminative algorithm for learni...

Enzo Ferrante, et al. ∙

research

∙ 07/18/2017

Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation

We propose an approach to discover class-specific pixels for the weakly-...

Arslan Chaudhry, et al. ∙

research

∙ 04/10/2017

Multi-Agent Diverse Generative Adversarial Networks

This paper describes an intuitive generalization to the Generative Adver...

Arnab Ghosh, et al. ∙

research

∙ 05/30/2016

Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs

In this paper, we propose several improvements on the block-coordinate F...

Anton Osokin, et al. ∙

research

∙ 07/05/2015

Parsimonious Labeling

We propose a new family of discrete energy minimization problems, which ...

Puneet K. Dokania, et al. ∙

Puneet K. Dokania

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