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Found 5 papers, 2 papers with code
D3GU: Multi-Target Active Domain Adaptation via Enhancing Domain Alignment
Unsupervised domain adaptation (UDA) for image classification has made remarkable progress in transferring classification knowledge from a labeled source domain to an unlabeled target domain, thanks to effective domain alignment techniques.
Active Learning for Video Classification with Frame Level Queries
To the best of our knowledge, this is the first research effort to develop an active learning framework for video classification, where the annotators need to inspect only a few frames to produce a label, rather than watching the end-to-end video.
Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation
To determine the parameters in the NET model (and in other unsupervised domain adaptation models), we introduce a validation procedure by sampling source data points that are similar in distribution to the target data.
Nonlinear Embedding Transform for Unsupervised Domain Adaptation
The problem of domain adaptation (DA) deals with adapting classifier models trained on one data distribution to different data distributions.
Deep Hashing Network for Unsupervised Domain Adaptation
Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain.
