Compositional Generative Networks and Robustness to Perceptible Image Changes Adam Kortylewski Ju He Qing Liu Christian Cosgrove Dept. Computer Science Dept. Computer Science Dept. Computer Science Dept. Computer Science Johns Hopkins University Johns Hopkins University Johns Hopkins University Johns Hopkins University Baltimore, USA Baltimore, USA Baltimore, USA Baltimore, USA
[email protected] [email protected] [email protected] [email protected] Chenglin Yang Alan L. Yuille Dept. Computer Science Dept. Computer Science Johns Hopkins University Johns Hopkins University Baltimore, USA Baltimore, USA
[email protected] [email protected] Abstract—Current Computer Vision algorithms for classifying paradigm of measuring research progress in computer vision objects, such as Deep Nets, lack robustness to image changes in terms of performance improvements on well-known datasets which, although perceptible, would not fool a human observer. for large-scale image classification [8], segmentation [10], We quantify this by showing how performances of Deep Nets de- grades badly on images where the objects are partially occluded [25], pose estimation [40], and part detection [4]. and degrades even worse on more challenging and adversarial However, the focus on dataset performance encourages situations where, for example, patches are introduced in the researchers to develop computer vision models that work well images to target the weak points of the algorithm. To address this on a particular dataset, but do not transfer well to other problem we develop a novel architecture, called Compositional datasets. We argue that this lack of robustness is caused by Generative Networks (Compositional Nets) which is innately robust to these types of image changes. This architecture replaces the paradigm of evaluating computer vision algorithms on the fully connected classification head of the deep network by balanced annotated datasets (BAD).