GAP-Prompt: Gated Adaptive Prompting for Efficient Continual Learning

arXiv:2608.23782 · cs.LG · Submitted 2026-08-24 · Read on arXiv

cs.LG

Submitted: 2026-08-24

Updated: 2026-08-24

Terminology

• Accuracy — 39× in this paper · explained in 1 episode
• Continual Learning — 26× in this paper · explained in 2 episodes
• Instance-Conditioned Gating — 15× in this paper
• Dynamic Knowledge Fusion — 12× in this paper
• Distillation — 8× in this paper · explained in 2 episodes
• Shared Prompt Distillation — 8× in this paper
• Transformer — 8× in this paper · explained in 2 episodes
• Aggregation — 7× in this paper · explained in 1 episode
• Benchmark — 7× in this paper · explained in 2 episodes
• Catastrophic Forgetting — 6× in this paper · explained in 5 episodes
• Adaptation — 5× in this paper · explained in 1 episode
• Cosine Similarity — 5× in this paper · explained in 3 episodes
• Regularization — 5× in this paper · explained in 3 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Computational overhead — 4× in this paper · explained in 2 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Adaptability — 3× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• OVOR: OnePrompt with Virtual Outlier Regularization for Rehearsal-Free Class-Incremental Learning
• Decoupled Weight Decay Regularization
• ImageNet-21K Pretraining for the Masses
• Lifelong Learning with Dynamically Expandable Networks

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