most citedLessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification

7 citations · 8 across the 3 of their papers we have counts for

collaborators

3 papers

cs.LG2022★ 1 cited

Attacking Adversarial Defences by Smoothing the Loss Landscape

Panagiotis Eustratiadis, Henry Gouk, Da Li +1

This paper investigates a family of methods for defending against adversarial attacks that owe part of their success to creating a noisy, discontinuous, or otherwise rugged loss la…

cs.LG2022

HyperInvariances: Amortizing Invariance Learning

Ruchika Chavhan, Henry Gouk, Jan Stühmer +1

Providing invariances in a given learning task conveys a key inductive bias that can lead to sample-efficient learning and good generalisation, if correctly specified. However, the…

cs.LG2022★ 7 cited

Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification

Adrian El Baz, Ihsan Ullah, Edesio Alcobaça +17

Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resourc…