Consistently Informative Soft-Label Temperature for Knowledge Distillation
The article discusses a new approach to knowledge distillation called Consistently Informative Soft-label Temperature (CIST). This method addresses the limitations of fixed-temperature designs by assigning adaptive temperatures to both teacher and student models. Empirical results show that CIST improves the consistency and effectiveness of knowledge transfer in machine learning tasks.
- ▪Knowledge distillation transfers knowledge from a teacher model to a student model using temperature scaling.
- ▪The standard fixed-temperature design can lead to inconsistent entropy in teacher soft labels.
- ▪CIST assigns separate sample-wise adaptive temperatures to improve the quality of teacher soft labels.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20357 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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Computer Science > Machine Learning arXiv:2605.20357 (cs) [Submitted on 19 May 2026] Title:Consistently Informative Soft-Label Temperature for Knowledge Distillation Authors:Hoang-Chau Luong, Nghia Van Vo, Kaiqi Zhao, Lingwei Chen View a PDF of the paper titled Consistently Informative Soft-Label Temperature for Knowledge Distillation, by Hoang-Chau Luong and 3 other authors View PDF HTML (experimental) Abstract:Knowledge distillation (KD) transfers knowledge from a high-capacity teacher to a compact student by matching their predictive distributions, with temperature scaling serving as a central mechanism for smoothing teacher predictions and exposing informative "dark knowledge" beyond the hard label. However, the standard fixed-temperature design is inherently sample-agnostic.
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