arXiv (All)
open access
Continual Distillation Learning for Rehearsal-Free Class-Incremental Learning via Decoupled Prompting
Prompt-based continual learning has shown strong performance in rehearsal-free class-incremental learning by adapting learnable prompts while freezing a pre-trained Vision Transformer (ViT) backbone. However, the effect of backbone scale remains underexplored. We observe that larger ViT backbones co
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Replay in the Silent Degrees of Freedom: Continual Learning Without an Offline Phase#1115586
- From Scores to Samples: Elastic Forcing for Autoregressive Video Generation#1144872
- Self-Aligned Forcing: Streaming Video Diffusion with Differentiable Noisy History#1142410
- Progressive Multimodal Alignment for Continual Instruction Tuning#411080
- How Do Large Language Models Learn Concepts During Continual Pre-Training?#629893
Record · ID 972871
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive