Summary of Darda: Domain-aware Real-time Dynamic Neural Network Adaptation, by Shahriar Rifat et al.
DARDA: Domain-Aware Real-Time Dynamic Neural Network Adaptation
by Shahriar Rifat, Jonathan Ashdown, Francesco Restuccia
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Test Time Adaptation (TTA) has been developed to address the issue of Deep Neural Networks (DNNs) degrading in performance when faced with input corruption or noise. Existing TTA approaches continuously adapt the DNN, resulting in excessive resource consumption and performance degradation due to accumulated errors without supervision. This paper proposes Domain-Aware Real-Time Dynamic Adaptation (DARDA), a novel approach that proactively learns latent representations of different corruption types, each associated with a sub-network state tailored for correct classification. DARDA adapts the DNN to new corruptions in an unsupervised manner by estimating the ongoing corruption’s latent representation, selecting the closest associated sub-network, and adapting the DNN state to match the ongoing corruption. This approach reduces energy consumption and cache memory footprint while increasing performance on popular datasets like CIFAR-10, CIFAR-100, and TinyImagenet. DARDA is a resource-efficient solution that can swiftly adapt to new distributions caused by different corruptions without requiring a large input dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a way to make Deep Neural Networks (DNNs) better at handling problems with the data they’re given. When DNNs get corrupted or noisy data, their performance drops. The current solution to this problem is called Test Time Adaptation (TTA), but it has some drawbacks. This new approach, called DARDA, tries to fix these issues by learning how different types of corruption affect the data and using that information to adapt the DNN on the fly. This makes the DNN more efficient and better at handling new types of corruption without needing a lot of training data. The results show that DARDA is able to reduce energy consumption and memory usage while improving performance. |
Keywords
» Artificial intelligence » Classification » Unsupervised
