New research explores optimal adapter placement in vision transformers

Researchers have explored two distinct methods for optimizing the placement of task-specific adapters in continual learning models, specifically within vision transformers. One approach, placement search, involves training adapters in various block configurations and observing an inverted U-shaped accuracy curve, peaking at intermediate depths. The other method, inspired by neuroscience, uses functional magnetic resonance imaging (fMRI) readouts from the visual cortex to guide adapter allocation without extensive search, demonstrating competitive performance with reduced storage and runtime. AI

IMPACT This research could lead to more efficient continual learning models by optimizing adapter placement, reducing storage needs and computational overhead.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new methodology for continual learning in vision transformers. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• arXiv
• AugReg
• Bilora
• cs.CV
• functional magnetic resonance imaging
• Ibot
• Laurie S. Balistrieri
• Two Routes to the Middle: Placement Search and Brain Readouts Converge on Where Continual Learners Should Specialize
• vision transformer
• Visual Cortex
• ViT-B/16

• paper
• model release

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