Dynamic LoRA-Experts and Prototype-Ensemble Matching for Class-Incremental Learning
cs.LG
Submitted: 2026-09-30
Updated: 2026-09-30
Code: https://github.com/hongwei-zhao/Applied_Sciences-DLEPEM-main
Sources
- Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey and Benchmark
- SD-LoRA: Scalable Decoupled Low-Rank Adaptation for Class Incremental Learning
- S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning
- Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning
- LoRA: Low-Rank Adaptation of Large Language Models
- LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
- When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks