Summary of Improving the Explain-any-concept by Introducing Nonlinearity to the Trainable Surrogate Model, By Mounes Zaval and Sedat Ozer
Improving the Explain-Any-Concept by Introducing Nonlinearity to the Trainable Surrogate Model
by Mounes Zaval, Sedat Ozer
First submitted to arxiv on: 20 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes an improvement to the Explain Any Concept (EAC) model, a flexible method for explaining deep neural network (DNN) decisions in computer vision tasks. The EAC model uses a surrogate model with one trainable linear layer to simulate the target DNN. In this work, an additional nonlinear layer is introduced to improve the performance of the EAC model on ImageNet and MS COCO datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves the Explain Any Concept (EAC) model by adding a nonlinear layer to its surrogate model. This makes it better at explaining DNN decisions in computer vision tasks like image classification and object detection. The improved EAC model does this more accurately than the original one on two big datasets: ImageNet and MS COCO. |
Keywords
» Artificial intelligence » Image classification » Neural network » Object detection
