  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. Our code and models are
released at https://github.com/TalalWasim/TextGuidedResilience.
