435 citations · 454 across the 6 of their papers we have counts for
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Better Practices for Domain Adaptation
Linus Ericsson, Da Li, Timothy M. Hospedales
Distribution shifts are all too common in real-world applications of machine learning. Domain adaptation (DA) aims to address this by providing various frameworks for adapting mode…
Self-Supervised Representation Learning: Introduction, Advances and Challenges
Linus Ericsson, Henry Gouk, Chen Change Loy +1
Self-supervised representation learning methods aim to provide powerful deep feature learning without the requirement of large annotated datasets, thus alleviating the annotation b…
Don't Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights
Linus Ericsson, Henry Gouk, Timothy M. Hospedales
In the absence of large labelled datasets, self-supervised learning techniques can boost performance by learning useful representations from unlabelled data, which is often more re…