435 citations · 454 across the 6 of their papers we have counts for
7 papers
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…
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity
Raman Dutt, Linus Ericsson, Pedro Sanchez +2
Foundation models have significantly advanced medical image analysis through the pre-train fine-tune paradigm. Among various fine-tuning algorithms, Parameter-Efficient Fine-Tuning…
Label-Efficient Object Detection via Region Proposal Network Pre-Training
Nanqing Dong, Linus Ericsson, Yongxin Yang +2
Self-supervised pre-training, based on the pretext task of instance discrimination, has fueled the recent advance in label-efficient object detection. However, existing studies foc…
Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks
Linus Ericsson, Henry Gouk, Timothy M. Hospedales
Self-supervised learning is a powerful paradigm for representation learning on unlabelled images. A wealth of effective new methods based on instance matching rely on data-augmenta…
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…
How Well Do Self-Supervised Models Transfer?
Linus Ericsson, Henry Gouk, Timothy M. Hospedales
Self-supervised visual representation learning has seen huge progress recently, but no large scale evaluation has compared the many models now available. We evaluate the transfer p…
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…