Gaussian-guided latent alignment approach to align the latent feature distributions of the two domains . In such an indirect way, distributions over samples will be constructed on a common feature space, i.e., the space of the prior, which promotes better feature alignment . The extensive evaluations on eight benchmark datasets validate the superior knowledge transferability through outperforming state-of-the-art methods and the versatility of the proposed method by improving the existing work significantly . The authors propose a novel unpaired L1-distance by taking advantage of the formulation of the form of the encoder-decoder. To effectively align the target latent distribution with this prior distribution, we also propose a Novel unpaired . L1 distance by . taking advantage of the . formulation of . the formulation . of the Formal Encoder-Decoder. It also proposes a novel unaired L 1-distance. to take advantage of a novel L1 Distance by taking . advantage of an encoder and a novel

Links: PDF - Abstract

Code :

https://github.com/JingWang18/Discriminative-Feature-Alignment

Keywords : distance - latent - alignment - advantage - feature -

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