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Deep neural networks can model images with rich latent representations, butthey cannot naturally conceptualize structures of object categories in ahuman-perceptible way. This paper addresses the problem of learning objectstructures in an image modeling process without supervision. We propose anautoencoding formulation to discover landmarks as explicit structuralrepresentations. The encoding module outputs landmark coordinates, whosevalidity is ensured by constraints that reflect the necessary properties forlandmarks. The decoding module takes the landmarks as a part of the learnableinput representations in an end-to-end differentiable framework. Our discoveredlandmarks are semantically meaningful and more predictive of manually annotatedlandmarks than those discovered by previous methods. The coordinates of ourlandmarks are also complementary features to pretrained deep-neural-networkrepresentations in recognizing visual attributes. In addition, the proposedmethod naturally creates an unsupervised, perceptible interface to manipulateobject shapes and decode images with controllable structures. The projectwebpage is at http://ytzhang.net/projects/lmdis-rep

Unsupervised Discovery of Object Landmarks as Structural Representations