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      Learning Disentangled Representations via Independent Subspaces

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          Abstract

          Image generating neural networks are mostly viewed as black boxes, where any change in the input can have a number of globally effective changes on the output. In this work, we propose a method for learning disentangled representations to allow for localized image manipulations. We use face images as our example of choice. Depending on the image region, identity and other facial attributes can be modified. The proposed network can transfer parts of a face such as shape and color of eyes, hair, mouth, etc.~directly between persons while all other parts of the face remain unchanged. The network allows to generate modified images which appear like realistic images. Our model learns disentangled representations by weak supervision. We propose a localized resnet autoencoder optimized using several loss functions including a loss based on the semantic segmentation, which we interpret as masks, and a loss which enforces disentanglement by decomposition of the latent space into statistically independent subspaces. We evaluate the proposed solution w.r.t. disentanglement and generated image quality. Convincing results are demonstrated using the CelebA dataset.

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          Most cited references 14

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          Image-to-Image Translation with Conditional Adversarial Networks

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            Deep Learning Face Attributes in the Wild

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              High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

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                Author and article information

                Journal
                26 August 2019
                Article
                1908.08989

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Accepted at ICCV 2019 Workshop on Robust Subspace Learning and Applications in Computer Vision
                cs.CV cs.LG stat.ML

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