MetaErr: Towards Predicting Error Patterns in Deep Neural Networks
Due to the unprecedented success of deep learning, it has become an integral component in several multimedia computing applications in todays world. Unfortunately, deep learning systems are not perfect and can fail, sometimes abruptly, without prior warning or explanation. While reducing the error rate of deep neural networks has been the primary focus of the multimedia community, the problem of predicting when a deep learning system is going to fail has received significantly less research attention. In this paper, we propose a simple yet effective framework, MetaErr, to address this under-explored problem in deep learning research. We train a meta-model whose goal is to predict whether ...
| Authors | Varun Totakura, Shayok Chakraborty |
|---|---|
| Primary category | cs.CV |
| Published | 25 April 2026 |
| Last updated at source | 3 May 2026 |
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