Summary of Mindset: Vision. a Toolbox For Testing Dnns on Key Psychological Experiments, by Valerio Biscione et al.
MindSet: Vision. A toolbox for testing DNNs on key psychological experiments
by Valerio Biscione, Dong Yin, Gaurav Malhotra, Marin Dujmovic, Milton L. Montero, Guillermo Puebla, Federico Adolfi, Rachel F. Heaton, John E. Hummel, Benjamin D. Evans, Karim Habashy, Jeffrey S. Bowers
First submitted to arxiv on: 8 Apr 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 proposed toolbox, MindSet: Vision, is a collection of image datasets and scripts designed to test deep neural networks (DNNs) on 30 psychological findings related to human visual perception and object recognition. The datasets are systematically manipulated to test specific hypotheses, allowing for configurable parameters that extend the dataset versatility for different research contexts. Three methods are provided for testing DNNs: similarity judgments, out-of-distribution classification, and decoder method. As an example, ResNet-152 is tested on each of these methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to test how well artificial intelligence (AI) models work when it comes to recognizing objects and understanding human vision. Right now, people use natural images to see if AI models are good at this task, but this doesn’t tell us much about what the models are actually doing. The MindSet: Vision toolbox helps change that by providing special datasets and tools to test AI models in a more controlled way. This can help scientists understand how AI models work and why they’re good or bad at certain tasks. |
Keywords
» Artificial intelligence » Classification » Decoder » Resnet
