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Oct, 2020
使用Wasserstein自编码器学习解耦表示
Learning disentangled representations with the Wasserstein Autoencoder
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Benoit Gaujac, Ilya Feige, David Barber
TL;DR
该论文提出了 TCWAE 方法,使用 WAE 框架在潜在变量上分离了总相关项,从而在保持重构精度的同时提供了对学习表示的解缠结控制,同时在选择重构成本方面提供了更大的灵活性,并在已知生成因素的数据集上进行了大量的量化比较,取得了与最先进技术相比具有竞争力的结果。
Abstract
disentangled representation learning
has undoubtedly benefited from objective function surgery. However, a delicate balancing act of tuning is still required in order to trade off
reconstruction fidelity
versus d
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