Towards interpretable-by-design deep learning algorithms

• URL: http://arxiv.org/abs/2311.11396v1
• Date: Sun, 19 Nov 2023 18:40:49 GMT
• Title: Towards interpretable-by-design deep learning algorithms
• Authors: Plamen Angelov, Dmitry Kangin, Ziyang Zhang
• Abstract summary: A proposed framework named I recasts the standard supervised classification problem into a function of similarity to a set of prototypes derived from the training data. We show that one can turn such DL models into conceptually simpler, explainable-through-prototypes ones.
• Score: 11.154826546951414
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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