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Representation Decomposition for Image Manipulation and Beyond
Representation disentanglement aims at learning interpretable features, so that the output can be recovered or manipulated accordingly.
Semantics-Guided Representation Learning with Applications to Visual Synthesis
Learning interpretable and interpolatable latent representations has been an emerging research direction, allowing researchers to understand and utilize the derived latent space for further applications such as visual synthesis or recognition.
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