Copertina

Do Pre-trained Models Benefit Equally in Continual Learning?

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Lee, Kuan-Ying, Zhong, Yuanyi, Wang, Yu-Xiong
Natura:	Preprint
Pubblicazione:	2022
Soggetti:	Computer Vision and Pattern Recognition Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning
di: Wang, Huiyi, et al.
Pubblicazione: (2024)

Realistic Continual Learning Approach using Pre-trained Models
di: Nasri, Nadia, et al.
Pubblicazione: (2024)

RanPAC: Random Projections and Pre-trained Models for Continual Learning
di: McDonnell, Mark D., et al.
Pubblicazione: (2023)

Is Pre-training Truly Better Than Meta-Learning?
di: Miranda, Brando, et al.
Pubblicazione: (2023)

STELLA: Continual Audio-Video Pre-training with Spatio-Temporal Localized Alignment
di: Lee, Jaewoo, et al.
Pubblicazione: (2023)

How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey
di: Qi, Yayun, et al.
Pubblicazione: (2024)

OCT Data is All You Need: How Vision Transformers with and without Pre-training Benefit Imaging
di: Han, Zihao, et al.
Pubblicazione: (2025)

Practical Continual Forgetting for Pre-trained Vision Models
di: Zhao, Hongbo, et al.
Pubblicazione: (2025)

Large-scale Pre-trained Models are Surprisingly Strong in Incremental Novel Class Discovery
di: Liu, Mingxuan, et al.
Pubblicazione: (2023)

Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks
di: Ding, Yuhe, et al.
Pubblicazione: (2024)

D4C: Data-Free Quantization for Contrastive Language-Image Pre-training Models
di: Zhang, Wenlun, et al.
Pubblicazione: (2025)

Continual Learning with Pre-Trained Models: A Survey
di: Zhou, Da-Wei, et al.
Pubblicazione: (2024)

SLCA++: Unleash the Power of Sequential Fine-tuning for Continual Learning with Pre-training
di: Zhang, Gengwei, et al.
Pubblicazione: (2024)

VisionTS++: Cross-Modal Time Series Foundation Model with Continual Pre-trained Vision Backbones
di: Shen, Lefei, et al.
Pubblicazione: (2025)

Efficient and Long-Tailed Generalization for Pre-trained Vision-Language Model
di: Shi, Jiang-Xin, et al.
Pubblicazione: (2024)

Do Your Best and Get Enough Rest for Continual Learning
di: Kang, Hankyul, et al.
Pubblicazione: (2025)

Multi-instance Learning as Downstream Task of Self-Supervised Learning-based Pre-trained Model
di: Matsuishi, Koki, et al.
Pubblicazione: (2025)

TExplain: Explaining Learned Visual Features via Pre-trained (Frozen) Language Models
di: Taghanaki, Saeid Asgari, et al.
Pubblicazione: (2023)

Split Adaptation for Pre-trained Vision Transformers
di: Wang, Lixu, et al.
Pubblicazione: (2025)

Domain Guidance: A Simple Transfer Approach for a Pre-trained Diffusion Model
di: Zhong, Jincheng, et al.
Pubblicazione: (2025)

Read Between the Layers: Leveraging Multi-Layer Representations for Rehearsal-Free Continual Learning with Pre-Trained Models
di: Ahrens, Kyra, et al.
Pubblicazione: (2023)

Pre-trained Visual Dynamics Representations for Efficient Policy Learning
di: Luo, Hao, et al.
Pubblicazione: (2024)

PILOT: A Pre-Trained Model-Based Continual Learning Toolbox
di: Sun, Hai-Long, et al.
Pubblicazione: (2023)

DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training
di: Wang, Zihao, et al.
Pubblicazione: (2024)

Epistemic Uncertainty Quantification For Pre-trained Neural Network
di: Wang, Hanjing, et al.
Pubblicazione: (2024)

Slot-Guided Adaptation of Pre-trained Diffusion Models for Object-Centric Learning and Compositional Generation
di: Akan, Adil Kaan, et al.
Pubblicazione: (2025)

Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers
di: Wang, Lirui, et al.
Pubblicazione: (2024)

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models
di: Wang, Yimu, et al.
Pubblicazione: (2025)

Resilient Contrastive Pre-training under Non-Stationary Drift
di: Yang, Xiaoyu, et al.
Pubblicazione: (2025)

Machine Unlearning on Pre-trained Models by Residual Feature Alignment Using LoRA
di: Qin, Laiqiao, et al.
Pubblicazione: (2024)

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

Incorporating Pre-trained Diffusion Models in Solving the Schrödinger Bridge Problem
di: Tang, Zhicong, et al.
Pubblicazione: (2025)

ModelGrow: Continual Text-to-Video Pre-training with Model Expansion and Language Understanding Enhancement
di: Rao, Zhefan, et al.
Pubblicazione: (2024)

SAFE: Slow and Fast Parameter-Efficient Tuning for Continual Learning with Pre-Trained Models
di: Zhao, Linglan, et al.
Pubblicazione: (2024)

Understanding Flatness in Generative Models: Its Role and Benefits
di: Lee, Taehwan, et al.
Pubblicazione: (2025)

Feature Unlearning for Pre-trained GANs and VAEs
di: Moon, Saemi, et al.
Pubblicazione: (2023)

Shortcutting Pre-trained Flow Matching Diffusion Models is Almost Free Lunch
di: Cai, Xu, et al.
Pubblicazione: (2025)

Benchmarking Few-shot Transferability of Pre-trained Models with Improved Evaluation Protocols
di: Luo, Xu, et al.
Pubblicazione: (2026)

Pre-trained Language Models Do Not Help Auto-regressive Text-to-Image Generation
di: Zhang, Yuhui, et al.
Pubblicazione: (2023)

Divide and not forget: Ensemble of selectively trained experts in Continual Learning
di: Rypeść, Grzegorz, et al.
Pubblicazione: (2024)