arXiv:2407. 13911v5 Announce Type: replace-cross Abstract: Prompt-based continual learning has shown strong performance in rehearsal-free class-incremental learning by adapting learnable prompts while freezing a pre-trained Vision Transformer (ViT) backbone.
By Qifan Zhang, Yunhui Guo, Yu Xiang
arXiv:2609.13199v1 Announce Type: new
Abstract: Knowledge distillation aims to improve the performance of lightweight student models by transferring knowledge from larger and more powerful teacher mo...
By Dawen Jiang, Zhishu Shen, Zeyu Liu, Tiehua Zhang
The paper investigates how knowledge distillation (KD) applied at intermediate layers of a neural network can affect overfitting and model performance. While traditional KD focuses on the final output, this study explores block‑wise KD across eleven datasets, finding that on standard datasets the last block suffices, but on fine‑grained, data‑scarce settings intermediate supervision significantly improves accuracy. The authors also analyze optimal supervision granularity using attention maps, Centered Kernel Alignment, and Grad‑CAM, and examine teacher‑student fine‑tuning strategies.
By Irene Trigueros-Lorca, Leonardo Concepci\'on, Christian Wagner, Isaac Triguero, Daniel Molina
arXiv:2608. 00129v1 Announce Type: new Abstract: Knowledge distillation (KD) is a widely utilized technique for transferring knowledge from a large model (the teacher) to a smaller model (the student).
By Tiancong Cheng, Ying Zhang, Zhiwen Yu, Yifang Yin, Bin Guo
Dyna‑DINO introduces a curriculum for Vision Transformer (ViT) knowledge distillation that uses the teacher’s intermediate feature maps as progressively harder targets, enabling a student to build foundational representations before tackling higher‑level abstractions. The approach accelerates convergence and improves performance across multiple tasks: on ImageNet‑100 the distilled ViT‑S reaches 90.1% accuracy (+12.24% over baseline), while on ImageNet‑1K it yields +3.9% and +6.09% gains on Oxford and Paris retrieval, +1.93% on semantic segmentation, and notable classification improvements. Additionally, the curriculum reduces training FLOPs by 25.1% and training time by 21% on ImageNet‑100 through early‑stopping of teacher inference.
By Jiaqi Zhang, Ashton Lee, Anthony Wong, John Zou, Sami BuGhanem, Randall Balestriero
arXiv:2606. 07474v1 Announce Type: new Abstract: Unsupervised Continual Learning (UCL) aims to enable neural networks to learn sequential tasks without labels or access to past data.
By Mohammadreza Sadeghi, Sareh Soleimani, Zihan Wang, Narges Armanfard