17 citations · 50 across the 17 of their papers we have counts for
3 papers · 1 filter
Disentangled Representation Learning
Xin Wang, Hong Chen, Si'ao Tang +2
Disentangled Representation Learning (DRL) aims to learn a model capable of identifying and disentangling the underlying factors hidden in the observable data in representation for…
NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results
Dustin Carrión-Ojeda, Hong Chen, Adrian El Baz +6
We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on "cross-domain" meta-learning. Meta-learning…
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…