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    Announcement

    Empirical Variational Autoencoder proposes sequential generation with one extra linear layer

    Its introduction says the added layer predicts the next prior, without VQVAEs or diffusion models.

    Kosta DerpanisKD
    Kaede ShioharaKS
    2 Sources, ,

    TLDR

    The announcement introduces Empirical Variational Autoencoder (EVA) and claims it can revive variational autoencoders for sequential generation. It says EVA needs just one extra linear layer to predict the next prior, without VQVAEs or diffusion models.

    Combined views

    11.6K

    2 Sources, first seen 7h ago

    Combined views

    11.6K

    2 Sources, first seen 7h ago

    151 likes
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    7h ago
    first seen 7h ago
    151 likes
    2 comments
    129 saves
    26 reposts
    2 comments
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    26 reposts

    2 Sources

    Kaede Shiohara@KaedeShioharaCSIntroducing a new generative model, Empirical Variational Autoencoder (EVA): We can revive VAEs for sequential generation only with an extra single linear layer that predicts the next prior without VQVAEs or diffusion models. Project page: https://mapooon.github.io/EVAPage/7h
    Kosta Derpanis@CSProfKGDRT @KaedeShioharaCS: Introducing a new generative model, Empirical Variational Autoencoder (EVA): We can revive VAEs for sequential genera…3h
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    2 Sources

    Kaede Shiohara@KaedeShioharaCSIntroducing a new generative model, Empirical Variational Autoencoder (EVA): We can revive VAEs for sequential generation only with an extra single linear layer that predicts the next prior without VQVAEs or diffusion models. Project page: https://mapooon.github.io/EVAPage/7h
    Kosta Derpanis@CSProfKGDRT @KaedeShioharaCS: Introducing a new generative model, Empirical Variational Autoencoder (EVA): We can revive VAEs for sequential genera…3h