  The recently advanced unsupervised learning approaches use the siamese-like
framework to compare two "views" from the same image for learning
representations. Making the two views distinctive is a core to guarantee that
unsupervised methods can learn meaningful information. However, such frameworks
are sometimes fragile on overfitting if the augmentations used for generating
two views are not strong enough, causing the over-confident issue on the
training data. This drawback hinders the model from learning subtle variance
and fine-grained information. To address this, in this work we aim to involve
the distance concept on label space in the unsupervised learning and let the
model be aware of the soft degree of similarity between positive or negative
pairs through mixing the input data space, to further work collaboratively for
the input and loss spaces. Despite its conceptual simplicity, we show
empirically that with the solution -- Unsupervised image mixtures (Un-Mix), we
can learn subtler, more robust and generalized representations from the
transformed input and corresponding new label space. Extensive experiments are
conducted on CIFAR-10, CIFAR-100, STL-10, Tiny ImageNet and standard ImageNet
with popular unsupervised methods SimCLR, BYOL, MoCo V1&V2, SwAV, etc. Our
proposed image mixture and label assignment strategy can obtain consistent
improvement by 1~3% following exactly the same hyperparameters and training
procedures of the base methods. Code is publicly available at
https://github.com/szq0214/Un-Mix.
