Visual Concept Networks: A Graph-Based Approach to Detecting Anomalous Data in Deep Neural Networks
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916412350529536 |
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| author | Ganguly, Debargha Gupta, Debayan Chaudhary, Vipin |
| author_facet | Ganguly, Debargha Gupta, Debayan Chaudhary, Vipin |
| contents | Deep neural networks (DNNs), while increasingly deployed in many applications, struggle with robustness against anomalous and out-of-distribution (OOD) data. Current OOD benchmarks often oversimplify, focusing on single-object tasks and not fully representing complex real-world anomalies. This paper introduces a new, straightforward method employing graph structures and topological features to effectively detect both far-OOD and near-OOD data. We convert images into networks of interconnected human understandable features or visual concepts. Through extensive testing on two novel tasks, including ablation studies with large vocabularies and diverse tasks, we demonstrate the method's effectiveness. This approach enhances DNN resilience to OOD data and promises improved performance in various applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18235 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Visual Concept Networks: A Graph-Based Approach to Detecting Anomalous Data in Deep Neural Networks Ganguly, Debargha Gupta, Debayan Chaudhary, Vipin Computer Vision and Pattern Recognition Machine Learning Deep neural networks (DNNs), while increasingly deployed in many applications, struggle with robustness against anomalous and out-of-distribution (OOD) data. Current OOD benchmarks often oversimplify, focusing on single-object tasks and not fully representing complex real-world anomalies. This paper introduces a new, straightforward method employing graph structures and topological features to effectively detect both far-OOD and near-OOD data. We convert images into networks of interconnected human understandable features or visual concepts. Through extensive testing on two novel tasks, including ablation studies with large vocabularies and diverse tasks, we demonstrate the method's effectiveness. This approach enhances DNN resilience to OOD data and promises improved performance in various applications. |
| title | Visual Concept Networks: A Graph-Based Approach to Detecting Anomalous Data in Deep Neural Networks |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2409.18235 |