2.9k citations · 12k across the 144 of their papers we have counts for
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Contrastive Representation Learning with Trainable Augmentation Channel
Masanori Koyama, Kentaro Minami, Takeru Miyato +1
In contrastive representation learning, data representation is trained so that it can classify the image instances even when the images are altered by augmentations. However, depen…
Active Testing: Sample-Efficient Model Evaluation
Jannik Kossen, Sebastian Farquhar, Yarin Gal +1
We introduce a new framework for sample-efficient model evaluation that we call active testing. While approaches like active learning reduce the number of labels needed for model t…
On Statistical Bias In Active Learning: How and When To Fix It
Sebastian Farquhar, Yarin Gal, Tom Rainforth
Active learning is a powerful tool when labelling data is expensive, but it introduces a bias because the training data no longer follows the population distribution. We formalize…