  The recent rise of unsupervised and self-supervised learning has dramatically
reduced the dependency on labeled data, providing effective image
representations for transfer to downstream vision tasks. Furthermore, recent
works employed these representations in a fully unsupervised setup for image
classification, reducing the need for human labels on the fine-tuning stage as
well. This work demonstrates that large-scale unsupervised models can also
perform a more challenging object segmentation task, requiring neither
pixel-level nor image-level labeling. Namely, we show that recent unsupervised
GANs allow to differentiate between foreground/background pixels, providing
high-quality saliency masks. By extensive comparison on standard benchmarks, we
outperform existing unsupervised alternatives for object segmentation,
achieving new state-of-the-art.
