Summary of Dlbacktrace: a Model Agnostic Explainability For Any Deep Learning Models, by Vinay Kumar Sankarapu et al.
DLBacktrace: A Model Agnostic Explainability for any Deep Learning Models
by Vinay Kumar Sankarapu, Chintan Chitroda, Yashwardhan Rathore, Neeraj Kumar Singh, Pratinav Seth
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces DLBacktrace, a novel technique for providing interpretability in deep learning models. The authors highlight the importance of understanding model decisions in high-stakes applications where transparency is crucial. To address this challenge, they present a comprehensive overview of their approach and benchmark its performance against established methods such as SHAP, LIME, and GradCAM. The results show that DLBacktrace effectively enhances understanding of model behavior across diverse tasks and domains, including MLPs, CNNs, and Transformer-based LLM models. The library is open-sourced and compatible with models developed in PyTorch and TensorFlow. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making deep learning models more understandable. Right now, these models are like “black boxes” that don’t explain how they make decisions. This makes it hard to trust the model’s output when it matters most. The authors want to change this by creating a new tool called DLBacktrace. It can help us understand how different types of deep learning models work and why they make certain predictions. They tested their tool against other methods and found that it works well across many different domains and tasks. |
Keywords
» Artificial intelligence » Deep learning » Transformer
