Cover Image

Densely Distilling Cumulative Knowledge for Continual Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shi, Zenglin, Liu, Pei, Su, Tong, Wu, Yunpeng, Liu, Kuien, Song, Yu, Wang, Meng
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Continual Distillation Learning: Knowledge Distillation in Prompt-based Continual Learning
by: Zhang, Qifan, et al.
Published: (2024)

Learning Dual Transformers for All-In-One Image Restoration from a Frequency Perspective
by: Chu, Jie, et al.
Published: (2024)

Forward-Backward Knowledge Distillation for Continual Clustering
by: Sadeghi, Mohammadreza, et al.
Published: (2024)

Uncertainty-Aware Multi-Expert Knowledge Distillation for Imbalanced Disease Grading
by: Tong, Shuo, et al.
Published: (2025)

Continual Learning of Achieving Forgetting-free and Positive Knowledge Transfer
by: Wang, Zhi, et al.
Published: (2026)

Improving the Transferability of Adversarial Examples by Inverse Knowledge Distillation
by: Wu, Wenyuan, et al.
Published: (2025)

Distill to Think, Foresee to Act: Cognitive-Physical Reinforcement Learning for Autonomous Driving
by: Wu, Yang, et al.
Published: (2026)

Dynamic Sub-graph Distillation for Robust Semi-supervised Continual Learning
by: Fan, Yan, et al.
Published: (2023)

Preview-based Category Contrastive Learning for Knowledge Distillation
by: Ding, Muhe, et al.
Published: (2024)

One-shot Federated Learning via Synthetic Distiller-Distillate Communication
by: Zhang, Junyuan, et al.
Published: (2024)

Decoupling Dark Knowledge via Block-wise Logit Distillation for Feature-level Alignment
by: Yu, Chengting, et al.
Published: (2024)

Pixel Distillation: A New Knowledge Distillation Scheme for Low-Resolution Image Recognition
by: Guo, Guangyu, et al.
Published: (2021)

Learning to Discover Knowledge: A Weakly-Supervised Partial Domain Adaptation Approach
by: Lan, Mengcheng, et al.
Published: (2024)

Multi-Level Knowledge Distillation and Dynamic Self-Supervised Learning for Continual Learning
by: Kim, Taeheon, et al.
Published: (2025)

Small Scale Data-Free Knowledge Distillation
by: Liu, He, et al.
Published: (2024)

Enriching Knowledge Distillation with Intra-Class Contrastive Learning
by: Yuan, Hua, et al.
Published: (2025)

Multi-Stage Knowledge Integration of Vision-Language Models for Continual Learning
by: Zhang, Hongsheng, et al.
Published: (2024)

Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency
by: Zheng, Kaiwen, et al.
Published: (2025)

Online Distillation with Continual Learning for Cyclic Domain Shifts
by: Houyon, Joachim, et al.
Published: (2023)

Distilled Datamodel with Reverse Gradient Matching
by: Ye, Jingwen, et al.
Published: (2024)

Improving Knowledge Distillation in Transfer Learning with Layer-wise Learning Rates
by: Kokane, Shirley, et al.
Published: (2024)

On Distilling the Displacement Knowledge for Few-Shot Class-Incremental Learning
by: Fang, Pengfei, et al.
Published: (2024)

Learnable Cross-modal Knowledge Distillation for Multi-modal Learning with Missing Modality
by: Wang, Hu, et al.
Published: (2023)

Distilling the Knowledge in Data Pruning
by: Ben-Baruch, Emanuel, et al.
Published: (2024)

Exploring CLIP's Dense Knowledge for Weakly Supervised Semantic Segmentation
by: Yang, Zhiwei, et al.
Published: (2025)

Continual Distillation of Teachers from Different Domains
by: Michel, Nicolas, et al.
Published: (2026)

Quantum-Gated Task-interaction Knowledge Distillation for Pre-trained Model-based Class-Incremental Learning
by: Li, Linjie, et al.
Published: (2026)

PracticalDG: Perturbation Distillation on Vision-Language Models for Hybrid Domain Generalization
by: Chen, Zining, et al.
Published: (2024)

Automated Learning of Semantic Embedding Representations for Diffusion Models
by: Jiang, Limai, et al.
Published: (2025)

An Attention-based Representation Distillation Baseline for Multi-Label Continual Learning
by: Menabue, Martin, et al.
Published: (2024)

Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts
by: Lou, Meng, et al.
Published: (2026)

FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation
by: Ma, Yuting, et al.
Published: (2024)

Dynamic Temperature Knowledge Distillation
by: Wei, Yukang, et al.
Published: (2024)

Dense Optimizer : An Information Entropy-Guided Structural Search Method for Dense-like Neural Network Design
by: Tianyuan, Liu, et al.
Published: (2024)

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning
by: Lou, Meng, et al.
Published: (2026)

Brain-Inspired Continual Learning-Robust Feature Distillation and Re-Consolidation for Class Incremental Learning
by: Khan, Hikmat, et al.
Published: (2024)

GRMLR: Knowledge-Enhanced Small-Data Learning for Deep-Sea Cold Seep Stage Inference
by: Zhou, Chenxu, et al.
Published: (2026)

MST-Distill: Mixture of Specialized Teachers for Cross-Modal Knowledge Distillation
by: Li, Hui, et al.
Published: (2025)

EDocNet: Efficient Datasheet Layout Analysis Based on Focus and Global Knowledge Distillation
by: Chen, Hong Cai, et al.
Published: (2025)

Improving Neural ODEs via Knowledge Distillation
by: Chu, Haoyu, et al.
Published: (2022)