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This paper addresses unsupervised few-shot object recognition, where alltraining images are unlabeled, and test images are divided into queries and afew labeled support images per object class of interest. The training and testimages do not share object classes. We extend the vanilla GAN with two lossfunctions, both aimed at self-supervised learning. The first is areconstruction loss that enforces the discriminator to reconstruct theprobabilistically sampled latent code which has been used for generating the"fake" image. The second is a triplet loss that enforces the discriminator tooutput image encodings that are closer for more similar images. Evaluation,comparisons, and detailed ablation studies are done in the context of few-shotclassification. Our approach significantly outperforms the state of the art onthe Mini-Imagenet and Tiered-Imagenet datasets.

A Self-supervised GAN for Unsupervised Few-shot Object Recognition