Summary of Neuralood: Improving Out-of-distribution Generalization Performance with Brain-machine Fusion Learning Framework, by Shuangchen Zhao et al.
NeuralOOD: Improving Out-of-Distribution Generalization Performance with Brain-machine Fusion Learning Framework
by Shuangchen Zhao, Changde Du, Hui Li, Huiguang He
First submitted to arxiv on: 27 Aug 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 Deep learning models, specifically Deep Neural Networks (DNNs), have excelled in traditional computer vision tasks. However, these models struggle when faced with out-of-distribution (OOD) data, resulting in a significant decrease in accuracy. Humans, on the other hand, maintain a low error rate despite encountering OOD scenes due to their stored prior cognitive knowledge. This paper proposes Brain-machine Fusion Learning (BMFL), leveraging multimodal learning methods to improve OOD generalization. A cross-attention mechanism fuses visual knowledge from CV models and human brain-derived prior knowledge. The proposed framework employs pre-trained visual neural encoding, eliminating the need for functional Magnetic Resonance Imaging (fMRI) data collection and processing. Additionally, a brain transformer extracts knowledge from fMRI data, and Pearson correlation coefficient maximization regularization improves fusion capabilities. Experimental results show that BMFL outperforms DINOv2 and baseline models on ImageNet-1k validation dataset and six curated OOD datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how deep learning models can learn to recognize objects even when they’re in unusual situations. Right now, these models are really good at recognizing things in normal scenarios, but they struggle when they encounter something new or unexpected. Humans are actually better at this than computers because we have a lot of prior knowledge and experience that helps us figure out what’s going on. The paper proposes a new way to improve computer vision by combining human brain-derived knowledge with visual information from cameras. This allows the computer to learn more effectively in unusual situations. The results show that this approach is better than existing methods at recognizing objects in different scenarios. |
Keywords
» Artificial intelligence » Cross attention » Deep learning » Generalization » Regularization » Transformer
