Restyling Unsupervised Concept Based Interpretable Networks with Generative Models
This work addresses the limitation of concept interpretability in large-scale image recognition, offering an interactive method for researchers and practitioners, though it is incremental as it builds on existing generative models.
The paper tackles the problem of visualizing and understanding unsupervised concept dictionaries in interpretable networks for large-scale images by mapping concept features to a pretrained generative model's latent space, resulting in high-quality visualizations and improved interpretation with quantitative validation on accuracy, reconstruction fidelity, and concept faithfulness across multiple benchmarks.
Developing inherently interpretable models for prediction has gained prominence in recent years. A subclass of these models, wherein the interpretable network relies on learning high-level concepts, are valued because of closeness of concept representations to human communication. However, the visualization and understanding of the learnt unsupervised dictionary of concepts encounters major limitations, especially for large-scale images. We propose here a novel method that relies on mapping the concept features to the latent space of a pretrained generative model. The use of a generative model enables high quality visualization, and lays out an intuitive and interactive procedure for better interpretation of the learnt concepts by imputing concept activations and visualizing generated modifications. Furthermore, leveraging pretrained generative models has the additional advantage of making the training of the system more efficient. We quantitatively ascertain the efficacy of our method in terms of accuracy of the interpretable prediction network, fidelity of reconstruction, as well as faithfulness and consistency of learnt concepts. The experiments are conducted on multiple image recognition benchmarks for large-scale images. Project page available at https://jayneelparekh.github.io/VisCoIN_project_page/