AIBullisharXiv – CS AI · 15h ago7/10
🧠
The Rescue Effect: Spatio-Semantic Early Exit Bypasses Quantization Collapse in CLIP
Researchers address a critical failure mode in quantized Vision-Language Models by proposing LRA-EE, a technique that uses early exit strategies to bypass noise-saturated layers in INT8 CLIP. The method improves zero-shot classification accuracy by 2.44 percentage points while reducing computational load by 13.4%, demonstrating that selective layer utilization can recover performance lost to quantization-induced representation collapse.