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- W4287122956 abstract "Normalizing flows are bijective mappings between inputs and latent representations with a fully factorized distribution. They are very attractive due to exact likelihood valuation and efficient sampling. However, their effective capacity is often insufficient since the bijectivity constraint limits the model width. We address this issue by incrementally padding intermediate representations with noise. We precondition the noise in accordance with previous invertible units, which we describe as cross-unit coupling. Our invertible glow-like modules increase the model expressivity by fusing a densely connected block with Nystrom self-attention. We refer to our architecture as DenseFlow since both cross-unit and intra-module couplings rely on dense connectivity. Experiments show significant improvements due to the proposed contributions and reveal state-of-the-art density estimation under moderate computing budgets." @default.
- W4287122956 created "2022-07-25" @default.
- W4287122956 creator A5073708578 @default.
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- W4287122956 date "2021-06-08" @default.
- W4287122956 modified "2023-09-29" @default.
- W4287122956 title "Densely connected normalizing flows" @default.
- W4287122956 doi "https://doi.org/10.48550/arxiv.2106.04627" @default.
- W4287122956 hasPublicationYear "2021" @default.
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