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.

中文翻译：

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巴洛双胞胎：通过减少冗余进行自我指导的学习

自我监督学习（SSL）通过大型计算机视觉基准上的监督方法正在迅速缩小差距。SSL的成功方法是学习不变于输入样本失真的表示形式。但是，这种方法经常出现的问题是琐碎的常量表示形式的存在。当前大多数方法都通过仔细的实现细节来避免这种崩溃的解决方案。我们提出了一个目标函数，通过测量两个相同网络的输出之间的互相关矩阵，该互相关矩阵由样本的失真版本提供，并使其与单位矩阵尽可能接近，从而自然避免了此类崩溃。这使得样本的失真版本的表示向量相似，同时最小化了这些向量的分量之间的冗余。由于神经科学家H. Barlow将冗余减少原理应用于一对相同的网络，因此该方法称为Barlow Twins。Barlow双胞胎不需要大批量生产，也不需要网络双胞胎之间的不对称性，例如预测器网络，梯度停止或重量更新的移动平均值。它允许使用非常高维的输出向量。Barlow Twins优于ImageNet上在低数据状态下进行半监督分类的方法，并且与具有线性分类器头的ImageNet分类以及分类和对象检测的传输任务的当前技术水平相当。Barlow双胞胎不需要大批量生产，也不需要网络双胞胎之间的不对称性，例如预测器网络，梯度停止或重量更新的移动平均值。它允许使用非常高维的输出向量。Barlow Twins优于ImageNet上在低数据状态下进行半监督分类的方法，并且与具有线性分类器头的ImageNet分类以及分类和对象检测的传输任务的当前技术水平相当。Barlow双胞胎不需要大批量生产，也不需要网络双胞胎之间的不对称性，例如预测器网络，梯度停止或重量更新的移动平均值。它允许使用非常高维的输出向量。Barlow Twins优于ImageNet上在低数据状态下进行半监督分类的方法，并且与具有线性分类器头的ImageNet分类以及分类和对象检测的传输任务的当前技术水平相当。