Unsupervised Transformation Learning via Convex Relaxations

NeurIPS 2017 Tatsunori B. HashimotoJohn C. DuchiPercy Liang

Our goal is to extract meaningful transformations from raw images, such as varying the thickness of lines in handwriting or the lighting in a portrait. We propose an unsupervised approach to learn such transformations by attempting to reconstruct an image from a linear combination of transformations of its nearest neighbors... (read more)

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