This paper presents Deep Integrated Explanations (DIX) - a universal method for explaining vision models. DIX generates explanation maps by integrating information from the intermediate representations of the model, coupled with their corresponding gradients. Through an extensive array of both objective and subjective evaluations spanning diverse tasks, datasets, and model configurations, we showcase the efficacy of DIX in generating faithful and accurate explanation maps, while surpassing current state-of-the-art methods.
Deep Integrated Explanations
Deep Integrated Explanations (DIX) integrate intermediate representations and gradients to generate faithful and accurate explanation maps for vision models, outperforming existing methods.
- Year
- 2023
- Venue
- arXiv 2023
- Authors
- 6
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- Abstract onlyARXIV-DEFAULT
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- Abstract & full text
- arxiv.org/abs/2310.15368v2ARXIV-DEFAULT
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