Recovering the latent factors of variation of high dimensional data has so far focused on simple synthetic settings. Mostly building on unsupervised and weakly-supervised objectives, prior work missed out on the positive implications for representation learning on real world data. In this work, we propose to leverage knowledge extracted from a diversified set of supervised tasks to learn a common disentangled representation. Assuming each supervised task only depends on an unknown subset of the factors of variation, we disentangle the feature space of a supervised multi-task model, with features activating sparsely across different tasks and information being shared as appropriate. Importantly, we never directly observe the factors of variations but establish that access to multiple tasks is sufficient for identifiability under sufficiency and minimality assumptions. We validate our approach on six real world distribution shift benchmarks, and different data modalities (images, text), demonstrating how disentangled representations can be transferred to real settings.

本文提出了一种基于多任务学习的有监督编码器，该编码器通过一个广泛的、多样化的有监督任务来学习一个共同的解缠表示，以从高维数据中恢复潜在因素的变化。该方法在多个真实情况下进行了验证，包括图像和文本数据等不同数据形式。

利用稀疏和共享特征激活进行分离式表征学习