435 citations · 454 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
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…