-
Scaling Vision with Sparse Mixture of Experts
Sparsely-gated Mixture of Experts networks (MoEs) have demonstrated exce...
06/10/2021 ∙ by Carlos Riquelme, et al. ∙
6
∙
share
read it
-
Correlated Input-Dependent Label Noise in Large-Scale Image Classification
Large scale image classification datasets often contain noisy labels. We...
05/19/2021 ∙ by Mark Collier, et al. ∙
0
∙
share
read it
-
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions
We develop and rigorously evaluate a deep learning based system that can...
04/08/2021 ∙ by Abhijit Guha Roy, et al. ∙
27
∙
share
read it
-
Supervised Transfer Learning at Scale for Medical Imaging
Transfer learning is a standard technique to improve performance on task...
01/14/2021 ∙ by Basil Mustafa, et al. ∙
30
∙
share
read it
-
Big Self-Supervised Models Advance Medical Image Classification
Self-supervised pretraining followed by supervised fine-tuning has seen ...
01/13/2021 ∙ by Shekoofeh Azizi, et al. ∙
22
∙
share
read it
-
Deep Ensembles for Low-Data Transfer Learning
In the low-data regime, it is difficult to train good supervised models ...
10/14/2020 ∙ by Basil Mustafa, et al. ∙
19
∙
share
read it
-
Scalable Transfer Learning with Expert Models
Transfer of pre-trained representations can improve sample efficiency an...
09/28/2020 ∙ by Joan Puigcerver, et al. ∙
11
∙
share
read it
-
Analysis of Softmax Approximation for Deep Classifiers under Input-Dependent Label Noise
Modelling uncertainty arising from input-dependent label noise is an inc...
03/15/2020 ∙ by Mark Collier, et al. ∙
5
∙
share
read it
-
Learning to Segment Medical Images with Scribble-Supervision Alone
Semantic segmentation of medical images is a crucial step for the quanti...
07/12/2018 ∙ by Yigit B. Can, et al. ∙
6
∙
share
read it
