Summary of Can Dense Connectivity Benefit Outlier Detection? An Odyssey with Nas, by Hao Fu et al.
Can Dense Connectivity Benefit Outlier Detection? An Odyssey with NAS
by Hao Fu, Tunhou Zhang, Hai Li, Yiran Chen
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 Dense Connectivity Search of Outlier Detector (DCSOD) is a novel paradigm that leverages Neural Architecture Search (NAS) to explore the dense connectivity of Convolutional Neural Networks (CNNs) for near-Out-of-Distribution (OOD) detection. The approach utilizes a hierarchical search space containing versatile convolution operators and dense connectivity, enabling flexible exploration of CNN architectures with diverse connectivity patterns. To improve evaluation quality on OOD detection during search, the authors propose evolving distillation based on multi-view feature learning explanation. This stabilizes training for OOD detection evaluation, leading to improved search quality. Experimental results demonstrate that DCSOD outperforms widely used architectures and previous NAS baselines on CIFAR benchmarks under OOD detection protocol. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new approach called Dense Connectivity Search of Outlier Detector (DCSOD) helps make Convolutional Neural Networks (CNNs) safer to use in real-world applications. Normally, these networks are tested to see if they can spot things that aren’t normal data. The problem is that current methods don’t consider the special structures used in building these networks, which makes it hard to get reliable results. DCSOD changes this by letting a computer search for the best possible network structure and configuration for detecting near-out-of-the-ordinary data. This leads to better results than previous approaches and even beats some of the best existing methods. |
Keywords
» Artificial intelligence » Cnn » Distillation
