{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hardware-resilience-properties-of-text-guided-1","title":"Hardware Resilience Properties of Text-Guided Image Classifiers","arxiv_id":"2311.14062","date":"2023-11-23","proceeding":"NeurIPS 2023 11","authors":["Syed Talal Wasim","Kabila Haile Soboka","Abdulrahman Mahmoud","Salman Khan","David Brooks","Gu-Yeon Wei"],"abstract":"This paper presents a novel method to enhance the reliability of image classification models during deployment in the face of transient hardware errors. By utilizing enriched text embeddings derived from GPT-3 with question prompts per class and CLIP pretrained text encoder, we investigate their impact as an initialization for the classification layer. Our approach achieves a remarkable $5.5\\times$ average increase in hardware reliability (and up to $14\\times$) across various architectures in the most critical layer, with minimal accuracy drop ($0.3\\%$ on average) compared to baseline PyTorch models. Furthermore, our method seamlessly integrates with any image classification backbone, showcases results across various network architectures, decreases parameter and FLOPs overhead, and follows a consistent training recipe. This research offers a practical and efficient solution to bolster the robustness of image classification models against hardware failures, with potential implications for future studies in this domain. 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