327 citations · 365 across the 10 of their papers we have counts for
23 papers
A Step Towards Efficient Evaluation of Complex Perception Tasks in Simulation
Jonathan Sadeghi, Blaine Rogers, James Gunn +4
There has been increasing interest in characterising the error behaviour of systems which contain deep learning models before deploying them into any safety-critical scenario. Howe…
Multilevel Knowledge Transfer for Cross-Domain Object Detection
Botos Csaba, Xiaojuan Qi, Arslan Chaudhry +2
Domain shift is a well known problem where a model trained on a particular domain (source) does not perform well when exposed to samples from a different domain (target). Unsupervi…
No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks
Shyamgopal Karthik, Ameya Prabhu, Puneet K. Dokania +1
There has been increasing interest in building deep hierarchy-aware classifiers that aim to quantify and reduce the severity of mistakes, and not just reduce the number of errors.…
On Batch Normalisation for Approximate Bayesian Inference
Jishnu Mukhoti, Puneet K. Dokania, Philip H. S. Torr +1
We study batch normalisation in the context of variational inference methods in Bayesian neural networks, such as mean-field or MC Dropout. We show that batch-normalisation does no…
Continual Learning in Low-rank Orthogonal Subspaces
Arslan Chaudhry, Naeemullah Khan, Puneet K. Dokania +1
In continual learning (CL), a learner is faced with a sequence of tasks, arriving one after the other, and the goal is to remember all the tasks once the continual learning experie…
How benign is benign overfitting?
Amartya Sanyal, Puneet K Dokania, Varun Kanade +1
We investigate two causes for adversarial vulnerability in deep neural networks: bad data and (poorly) trained models. When trained with SGD, deep neural networks essentially achie…