An Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection

24 Jan 2020Rujikorn CharakornYuttapong ThawornwattanaSirawaj ItthipuripatNick PawlowskiPoramate ManoonpongNat Dilokthanakul

Visual data can be understood at different levels of granularity, where global features correspond to semantic-level information and local features correspond to texture patterns. In this work, we propose a framework, called SPLIT, which allows us to disentangle local and global information into two separate sets of latent variables within the variational autoencoder (VAE) framework... (read more)

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