"knowledge distillation" Papers
210 papers found • Page 3 of 5
Conference
TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models
Makoto Shing, Kou Misaki, Han Bao et al.
Task-Specific Zero-shot Quantization-Aware Training for Object Detection
Changhao Li, Xinrui Chen, Ji Wang et al.
Temporal Separation with Entropy Regularization for Knowledge Distillation in Spiking Neural Networks
Kairong Yu, Chengting Yu, Tianqing Zhang et al.
Test-Time Ensemble via Linear Mode Connectivity: A Path to Better Adaptation
Byungjai Kim, Chanho Ahn, Wissam Baddar et al.
TinySAM: Pushing the Envelope for Efficient Segment Anything Model
Han Shu, Wenshuo Li, Yehui Tang et al.
Token-Level Self-Play with Importance-Aware Guidance for Large Language Models
Tue Le, Hoang Tran, Quyen Tran et al.
Tripartite Weight-Space Ensemble for Few-Shot Class-Incremental Learning
Juntae Lee, Munawar Hayat, Sungrack Yun
TULIP: Token-length Upgraded CLIP
Ivona Najdenkoska, Mohammad Mahdi Derakhshani, Yuki Asano et al.
Turbo3D: Ultra-fast Text-to-3D Generation
Hanzhe Hu, Tianwei Yin, Fujun Luan et al.
U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening
Sungpyo Kim, Jeonghyeok Do, Jaehyup Lee et al.
UniCoTT: A Unified Framework for Structural Chain-of-Thought Distillation
Xianwei Zhuang, Zhihong Zhu, Zhichang Wang et al.
Universal Cross-Tokenizer Distillation via Approximate Likelihood Matching
Benjamin Minixhofer, Ivan Vulić, Edoardo Maria Ponti
Unlocking SLM Potential for Data Analysis Code Generation via Non-Parametric Knowledge Distillation
Jinyang Li, Jack Williams, Nick McKenna et al.
VA-MoE: Variables-Adaptive Mixture of Experts for Incremental Weather Forecasting
Hao Chen, Tao Han, Song Guo et al.
Vision‑Language‑Vision Auto‑Encoder: Scalable Knowledge Distillation from Diffusion Models
Tiezheng Zhang, Yitong Li, Yu-Cheng Chou et al.
What Makes a Good Dataset for Knowledge Distillation?
Logan Frank, Jim Davis
Why Knowledge Distillation Works in Generative Models: A Minimal Working Explanation
Sungmin Cha, Kyunghyun Cho
Active Object Detection with Knowledge Aggregation and Distillation from Large Models
Dejie Yang, Yang Liu
AdaDistill: Adaptive Knowledge Distillation for Deep Face Recognition
Fadi Boutros, Vitomir Struc, Naser Damer
Adaptive Multi-task Learning for Few-shot Object Detection
Yan Ren, Yanling Li, Wai-Kin Adams Kong
Adversarially Robust Distillation by Reducing the Student-Teacher Variance Gap
Junhao Dong, Piotr Koniusz, Junxi Chen et al.
AltDiffusion: A Multilingual Text-to-Image Diffusion Model
Fulong Ye, Guang Liu, Xinya Wu et al.
AMD: Automatic Multi-step Distillation of Large-scale Vision Models
Cheng Han, Qifan Wang, Sohail A Dianat et al.
AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge Distillation
Zihao Tang, Zheqi Lv, Shengyu Zhang et al.
Bayesian Knowledge Distillation: A Bayesian Perspective of Distillation with Uncertainty Quantification
Luyang Fang, Yongkai Chen, Wenxuan Zhong et al.
BKDSNN: Enhancing the Performance of Learning-based Spiking Neural Networks Training with Blurred Knowledge Distillation
Zekai Xu, Kang You, Qinghai Guo et al.
Boosting Residual Networks with Group Knowledge
Shengji Tang, Peng Ye, Baopu Li et al.
Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models
Weiwei Cao, Jianpeng Zhang, Yingda Xia et al.
Bridge Past and Future: Overcoming Information Asymmetry in Incremental Object Detection
QIJIE MO, Yipeng Gao, Shenghao Fu et al.
Building Variable-Sized Models via Learngene Pool
Boyu Shi, Shiyu Xia, Xu Yang et al.
COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems
Hao Tian, Sourav Medya, Wei Ye
Cooperative Knowledge Distillation: A Learner Agnostic Approach
Michael Livanos, Ian Davidson, Stephen Wong
CSL: Class-Agnostic Structure-Constrained Learning for Segmentation including the Unseen
Hao Zhang, Fang Li, Lu Qi et al.
Data-free Distillation of Diffusion Models with Bootstrapping
Jiatao Gu, Chen Wang, Shuangfei Zhai et al.
De-confounded Data-free Knowledge Distillation for Handling Distribution Shifts
Yuzheng Wang, Dingkang Yang, Zhaoyu Chen et al.
DeiT-LT: Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets
Harsh Rangwani, Pradipto Mondal, Mayank Mishra et al.
DetKDS: Knowledge Distillation Search for Object Detectors
Lujun Li, Yufan Bao, Peijie Dong et al.
DFD: Distilling the Feature Disparity Differently for Detectors
Kang Liu, Yingyi Zhang, Jingyun Zhang et al.
Direct Distillation between Different Domains
Jialiang Tang, Shuo Chen, Gang Niu et al.
Distilling Autoregressive Models to Obtain High-Performance Non-autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed
Yubin Xiao, Di Wang, Boyang Li et al.
Distilling Knowledge from Large-Scale Image Models for Object Detection
Gang Li, Wenhai Wang, Xiang Li et al.
Distilling ODE Solvers of Diffusion Models into Smaller Steps
Sanghwan Kim, Hao Tang, Fisher Yu
Distilling Semantic Priors from SAM to Efficient Image Restoration Models
Quan Zhang, Xiaoyu Liu, Wei Li et al.
DistiLLM: Towards Streamlined Distillation for Large Language Models
Jongwoo Ko, Sungnyun Kim, Tianyi Chen et al.
DistilVPR: Cross-Modal Knowledge Distillation for Visual Place Recognition
Sijie Wang, Rui She, Qiyu Kang et al.
Do Topological Characteristics Help in Knowledge Distillation?
Jungeun Kim, Junwon You, Dongjin Lee et al.
DSD-DA: Distillation-based Source Debiasing for Domain Adaptive Object Detection
Yongchao Feng, Shiwei Li, Yingjie Gao et al.
DSMix: Distortion-Induced Saliency Map Based Pre-training for No-Reference Image Quality Assessment
Jinsong Shi, Jinsong Shi, Xiaojiang Peng et al.
Dynamic Sub-graph Distillation for Robust Semi-supervised Continual Learning
Yan Fan, Yu Wang, Pengfei Zhu et al.
DεpS: Delayed ε-Shrinking for Faster Once-For-All Training
Aditya Annavajjala, Alind Khare, Animesh Agrawal et al.