<p>For the pedestrian attributes recognition, we demonstrate that deep models can memorize the pattern of attributes co-occurrences inherent to dataset, whether through explicit or implicit means. However, since the attributes interdependency is highly variable and unpredictable across different scenarios, the modeled attributes co-occurrences de facto serve as a data selection bias that hardly generalizes onto out-of-distribution samples. To address this thorny issue, we formulate a novel concept of attributes-disentangled feature learning, by which the mutual information among features of different attributes is minimized, ensuring the recognition of an attribute independent to the presence of others. Stemming from it, practical approaches are developed to effectively decouple attributes by suppressing the shared feature factors among attributes-specific features. As compelling merits, our method is exercised with minimal test-time computation, and is also highly extendable. With slight modifications on it, further improvements regarding better exploration of the feature space, softening the issue of imbalanced attributes distribution in dataset and flexibility in term of preserving certain causal attributes interdependencies can be achieved. Comprehensive experiments on various realistic datasets, such as PA100k, PETAzs and RAPzs, validate the efficacy and a spectrum of superiorities of our method.</p>