  Self-supervised learning (SSL) is rapidly closing the gap with supervised
methods on large computer vision benchmarks. A successful approach to SSL is to
learn representations which are invariant to distortions of the input sample.
However, a recurring issue with this approach is the existence of trivial
constant representations. Most current methods avoid such collapsed solutions
by careful implementation details. We propose an objective function that
naturally avoids such collapse by measuring the cross-correlation matrix
between the outputs of two identical networks fed with distorted versions of a
sample, and making it as close to the identity matrix as possible. This causes
the representation vectors of distorted versions of a sample to be similar,
while minimizing the redundancy between the components of these vectors. The
method is called Barlow Twins, owing to neuroscientist H. Barlow's
redundancy-reduction principle applied to a pair of identical networks. Barlow
Twins does not require large batches nor asymmetry between the network twins
such as a predictor network, gradient stopping, or a moving average on the
weight updates. It allows the use of very high-dimensional output vectors.
Barlow Twins outperforms previous methods on ImageNet for semi-supervised
classification in the low-data regime, and is on par with current state of the
art for ImageNet classification with a linear classifier head, and for transfer
tasks of classification and object detection.
