Unsupervised Transformation Learning via Convex Relaxations
By Tatsunori B. Hashimoto, Percy Liang, John C. Duchi. 2017
12 citations recorded by the source corpus.
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. On handwritten digits and celebrity portraits, we show that even with linear transformations, our method generates visually high-quality modified images. Moreover, since our method is semiparametric and does not model the data distribution, the learned transformations extrapolate off the training data and can be applied to new types of images.
Subfields: Computer Vision and Pattern Recognition
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In Computer Vision and Pattern Recognition
Citations in the corpus
- Conditional variance penalties and domain shift robustness (2017, background): “, 2015; He et al., 2015). Issues can arise when the learned representations rely on dependencies that vanish in test distributions (see for example Quionero-Candela et al. (2009); Torralba and Efros (2011); Csurka (2017) and references therein).”
- Guarding Against Adversarial Domain Shifts with Counterfactual Regularization (2017, background): “mage. 33 invariances from data and the focus often lies on geometric transformations of the input data or explicitly creating augmented observations (Sohn and Lee, 2012; Khasanova and Frossard, 2017; Hashimoto et al., 2017; Devries and Taylor, 2017). The main dierence between this line of work and C”