Contrastive learning has recently emerged as a promising approach for learning data representations that discover and disentangle the explanatory factors of the data. Previous analyses of such approaches have largely focused on individual contrastive losses, such as noise-contrastive estimation (NCE) and InfoNCE, and rely on specific assumptions about the data generating process. This paper extends the theoretical guarantees for disentanglement to a broader family of contrastive methods, while also relaxing the assumptions about the data distribution. Specifically, we prove identifiability of the true latents for four contrastive losses studied in this paper, without imposing common independence assumptions. The theoretical findings are validated on several benchmark datasets. Finally, practical limitations of these methods are also investigated.

翻译：对比学习近来作为一种有前景的数据表示学习方法崭露头角，该方法能够发现并解耦数据中的解释性因素。以往对此类方法的分析主要集中于单个对比损失函数（如噪声对比估计NCE和InfoNCE），且依赖于对数据生成过程的具体假设。本文将解耦的理论保证推广至更广泛的对比方法族，同时放宽了对数据分布的假设条件。具体而言，我们证明了本文研究的四种对比损失函数在不施加常见独立性假设的情况下，能够实现真实隐变量的可辨识性。该理论发现已在多个基准数据集上得到验证。最后，本文还探讨了这些方法的实际局限性。