Jul 17, 2020 · We propose a novel Domain2Vec model to provide vectorial representations of visual domains based on joint learning of feature disentanglement and Gram matrix.
Our domain embedding can be used to reason about the space of domains and solve many unsupervised domain adaptation problems. As a motivating example, we study ...
A technique to measure domain similarity is critical for domain adaptation performance. To describe and learn relations between different domains, we propose a ...
A technique to measure domain similarity is critical for domain adaptation performance. To describe and learn relations between different domains, we propose a ...
Jul 17, 2020 · This work proposes a novel Domain2Vec model to provide vectorial representations of visual domains based on joint learning of feature ...
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Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation [ECCV2020] ... Overcoming Label Noise for Source-free Unsupervised Video Domain Adaptation ...
[ECCV/2020] Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation: disentangle [domain + category] + 2 GAN CODE; [ECCV/2020] Unsupervised Domain ...
Jun 10, 2024 · We introduce an algorithm for tackling the problem of unsupervised domain adaptation (UDA) in continual learning (CL) scenarios.
Jan 4, 2024 · We use these embeddings for two primary purposes: 1) categorizing semantically similar domains and 2) creating customer embeddings, which are ...
The 2017 Visual Domain Adaptation (VisDA) dataset and challenge, a large-scale testbed for unsupervised domain adaptation across visual domains, is presented