Colorization as a Proxy Task for Visual Understanding

Gustav Larsson, Michael Maire, Gregory Shakhnarovich
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
We investigate and improve self-supervision as a dropin replacement for ImageNet pretraining, focusing on automatic colorization as the proxy task. Self-supervised training has been shown to be more promising for utilizing unlabeled data than other, traditional unsupervised learning methods. We build on this success and evaluate the ability of our self-supervised network in several contexts. On VOC segmentation and classification tasks, we present results that are state-of-the-art among methods
more » ... e-art among methods not using Im-ageNet labels for pretraining representations. Moreover, we present the first in-depth analysis of selfsupervision via colorization, concluding that formulation of the loss, training details and network architecture play important roles in its effectiveness. This investigation is further expanded by revisiting the ImageNet pretraining paradigm, asking questions such as: How much training data is needed? How many labels are needed? How much do features change when fine-tuned? We relate these questions back to self-supervision by showing that colorization provides a similarly powerful supervisory signal as various flavors of ImageNet pretraining.
doi:10.1109/cvpr.2017.96 dblp:conf/cvpr/LarssonMS17 fatcat:qwfv3pqovrhlbarjtovpfxroxq