Self-ensembling for visual domain adaptation
read the original abstract
This paper explores the use of self-ensembling for visual domain adaptation problems. Our technique is derived from the mean teacher variant (Tarvainen et al., 2017) of temporal ensembling (Laine et al;, 2017), a technique that achieved state of the art results in the area of semi-supervised learning. We introduce a number of modifications to their approach for challenging domain adaptation scenarios and evaluate its effectiveness. Our approach achieves state of the art results in a variety of benchmarks, including our winning entry in the VISDA-2017 visual domain adaptation challenge. In small image benchmarks, our algorithm not only outperforms prior art, but can also achieve accuracy that is close to that of a classifier trained in a supervised fashion.
This paper has not been read by Pith yet.
Forward citations
Cited by 3 Pith papers
-
Revisiting Shadow Detection from a Vision-Language Perspective
SVL uses language embeddings aligned with global image representations via shadow ratio regression and global-to-local coupling to improve shadow detection robustness in ambiguous cases.
-
Adaptive Camera Sensor for Vision Models
Lens adapts camera sensors in real time via the VisiT confidence-based quality indicator to improve vision model accuracy on domain-shifted images, shown on ImageNet-ES and a new diverse benchmark.
-
Unsupervised Domain Adaptation via Calibrating Uncertainties
A new regularization approach for unsupervised domain adaptation that calibrates Renyi entropy of uncertainties estimated via variational Bayes.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.