arXiv Machine Learning
2d ago

Adaptive Dual-Teacher Distillation with Subnetwork Rectification for Bridging Semantic Gaps in Black-Box Domain Adaptation

The paper introduces Adaptive Dual-Teacher Distillation with Subnetwork Rectification (DDSR), a method for black-box domain adaptation that fuses predictions from a black-box source model and a vision‑language model to generate reliable pseudo‑labels for the target domain. DDSR uses adaptive prediction fusion, subnetwork regularization to prevent overfitting, iterative refinement of pseudo‑labels and prompts, and class‑wise prototypes for self‑training. Experiments on several benchmark datasets show that DDSR outperforms existing state‑of‑the‑art approaches, even those that have access to source data or source model parameters.

By Zhe Zhang, Jing Li, Wanli Xue, Xu Cheng, Jianhua Zhang, Qinghua Hu, Shengyong Chen
arXiv AI
Jun 26

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes

arXiv:2509. 09960v2 Announce Type: replace-cross Abstract: Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-quality tabular data is insufficient.

By Mingxuan Jiang, Keyang Chen, Yongxin Wang, Yongsheng Zhao, Ziyue Dai, Yicun Liu, Zeping Li, Qiuyang Zhang, Hongyi Nie, Hongbin Zhu, Sen Liu, Guangnan Ye, Hongfeng Chai
arXiv AI
Aug 11

DeltaPrompts: Escaping the Zero-Delta Trap in Multimodal Distillation

arXiv:2605. 15532v3 Announce Type: replace-cross Abstract: Distillation enables compact Vision-Language Models (VLMs) to obtain strong reasoning capabilities, yet the prompts driving this process are typically chosen via simple heuristics or aggregated from off-the-shelf datasets.

By Jaehun Jung, Hyunwoo Kim, Brandon Cui, Ximing Lu, David Acuna, Prithviraj Ammanabrolu, Yejin Choi
arXiv Machine Learning
Jun 4

Breaking the Scale Barrier: One-Shot Knowledge Transfer via Frequency Transform

arXiv:2603. 07523v3 Announce Type: replace Abstract: Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a pre-trained model is tightly coupled with monolithic architecture, which restricts flexible reuse across models of varying scales.

By Jianlu Shen, Fu Feng, Yucheng Xie, Jiaqi Lv, Xin Geng