Restyling Unsupervised Concept Based Interpretable Networks with Generative Models

We introduce a generative model with an intrinsically interpretable layer-a concept bottleneck layer † -that constrains the model to encode human-understandable concepts. The concept bottleneck layer partitions the generative model into three parts: the pre-concept bottleneck portion, the CB layer, and the post-concept bottleneck portion. To train CB generative models, we complement the traditional task-based loss function for training generative models with a concept loss and an orthogonality loss. The CB layer and these loss terms are model agnostic, which we demonstrate by applying the CB layer to three different families of generative models: generative adversarial networks, variational autoencoders, and diffusion models. On multiple datasets across different types of generative models, steering a generative model, with the CB layer, outperforms all baselines-in some cases, it is 10 times more effective. In addition, we show how the CB layer can be used to interpret the output of the generative model and debug the model during or post training.