Towards interpretable-by-design deep learning algorithms

TL;DR

The paper introduces IDEAL, a framework that makes deep learning models more interpretable by using prototypes, while maintaining the benefits of large pre-trained models and enabling efficient class-incremental learning.

Contribution

It presents a novel prototype-based interpretability framework that leverages foundation models, addressing explainability, catastrophic forgetting, and transfer learning without finetuning.

Findings

Models are interpretable through prototypes.

IDEAL enables efficient class-incremental learning.

ViT architectures improve transfer learning speed without finetuning.

Abstract

The proposed framework named IDEAL (Interpretable-by-design DEep learning ALgorithms) recasts the standard supervised classification problem into a function of similarity to a set of prototypes derived from the training data, while taking advantage of existing latent spaces of large neural networks forming so-called Foundation Models (FM). This addresses the issue of explainability (stage B) while retaining the benefits from the tremendous achievements offered by DL models (e.g., visual transformers, ViT) pre-trained on huge data sets such as IG-3.6B + ImageNet-1K or LVD-142M (stage A). We show that one can turn such DL models into conceptually simpler, explainable-through-prototypes ones. The key findings can be summarized as follows: (1) the proposed models are interpretable through prototypes, mitigating the issue of confounded interpretations, (2) the proposed IDEAL framework circumvents the issue of catastrophic forgetting allowing efficient class-incremental learning, and (3) the proposed IDEAL approach demonstrates that ViT architectures narrow the gap between finetuned and non-finetuned models allowing for transfer learning in a fraction of time \textbf{without} finetuning of the feature space on a target dataset with iterative supervised methods.