Class-Level Feature Disentanglement for Multi-Label Image Classification

Generally, the interpretability of deep neural networks is categorized into a priori and a posteriori interpretability. A priori interpretability involves improving model transparency through deliberate design prior to training. Feature disentanglement is a method for achieving a priori interpretability. Existing disentanglement methods mostly focus on semantic features, such as intrinsic and shared features. These methods distinguish between the background and the main subject, but overlook class-level features in images. To address this, we take a further step by advancing feature disentanglement to the class level. For multi-label image classification tasks, we propose a class-level feature disentanglement method. Specifically, we introduce a multi-head classifier within the feature extraction layer of the backbone network to disentangle features. Each head in this classifier corresponds to a specific class and generates independent predictions, thereby guiding the model to better leverage the intrinsic features of each class and improving multi-label classification precision. Experiments demonstrate that our method significantly enhances performance metrics across various benchmarks while simultaneously achieving a priori interpretability.