We investigate and improve self-supervision as a drop-in 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 not using ImageNet labels for
pretraining representations.
Moreover, we present the first in-depth analysis of self-supervision 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.Comment: CVPR 2017 (Project page:
http://people.cs.uchicago.edu/~larsson/color-proxy/