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Nov 20, 2018 · The encoder aims at synthesizing real-like visual features while the decoder forces both the real and the synthesized visual features to be more ...
Experimental results on four benchmark datasets show that the proposed approach to synthesize visual features based on an auto-encoder framework paired with ...
In this work, we formulate a novel bi-shifting semantic auto-encoder to learn the semantic representations of the target instances and reinforce the ...
Yunlong Yu, Zhong Ji, Yanwei Pang, Jichang Guo, Zhongfei Zhang, Fei Wu: Bi-Adversarial Auto-Encoder for Zero-Shot Learning. CoRR abs/1811.08103 (2018).
Thus, our model can capture the generalized semantic characteristics related with the seen and unseen classes to alleviate the projection function problem.
Our model learns to generate adversarial semantic embeddings of the unknown classes to train an unknowns-informed ZS-OSR classifier.
The encoder aims at generating real-like visual features while the decoder forces both the real and the generated visual features to be more related to the ...
We present a domain adaptation based generative frame- work for zero-shot learning. Our framework addresses the problem of domain shift between the seen and ...
Bi-adversarial auto-encoder. (BAAE) [198] pairs an autoencoder with two adversarial networks. On one hand, the encoder, which operates as a generator ...
Sep 3, 2024 · Adversarial Autoencoders are an advanced type of autoencoder that integrate the principles of adversarial training to impose a prior distribution on the latent ...
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