Summary of Efficient Visualization Of Neural Networks with Generative Models and Adversarial Perturbations, by Athanasios Karagounis
Efficient Visualization of Neural Networks with Generative Models and Adversarial Perturbations
by Athanasios Karagounis
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 This paper presents a novel approach for deep visualization via a generative network, which simplifies the architecture by reducing the number of networks used. The model requires less prior training knowledge and uses a non-adversarial training process, where the discriminator acts as a guide rather than a competitor to the generator. The core contribution is its ability to generate detailed visualization images that align with specific class labels, using a unique skip-connection-inspired block design that enhances label-directed image generation. Additionally, the generated visualizations can be utilized as adversarial examples, effectively fooling classification networks with minimal perceptible modifications. Experimental results demonstrate that the method outperforms traditional adversarial example generation techniques in both targeted and non-targeted attacks, achieving up to a 94.5% fooling rate with minimal perturbation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating new ways to visualize data using artificial intelligence. It’s like drawing a picture that tells you what something is, like a label. The authors came up with a new way to do this by simplifying the process and making it easier to train the model. They also showed that these visualizations can be used to trick other AI systems into thinking something is true when it’s not. This is important because it helps us understand how AI works and where it might go wrong. |
Keywords
» Artificial intelligence » Classification » Image generation
