初期手がかり精度が視覚的ショートカット学習を形作る：制御された手がかり操作ベンチマークによる検証
Early Cue Precision Shapes Visual Shortcut Learning in Controlled Cue-Manipulation Benchmarks
視覚分類器が低レベルな手がかりに依存して失敗する現象が、学習初期の手がかり精度によってどう影響されるかを、合成タスクやCIFAR-10ベンチマークで体系的に検証した論文。
著者: Chanho Park, Woochan Lee, Janyeong Oh, Geongho Gong, Minshu Kim, Yeachan Kwak, Seongim Choi
分類: cs.CV, cs.AI
原文アブストラクト
Visual classifiers can achieve high matched-distribution accuracy while relying on low-level cues that fail under conflict or suppression. We test whether this failure is shaped by early cue precision: the reliability with which a low-level cue predicts the label during early learning or downstream probe fitting. Across synthetic shape-texture tasks, sequential digit training, a 10-class frozen-representation audit, and a CIFAR-10 natural-image-based texture-overlay benchmark, we manipulate object-texture match probability and evaluate matched-ID accuracy, conflict accuracy, texture-choice rate, and suppression behavior. Degraded-but-predictive input does not substitute for cue decorrelation. In 10-class digit probes, conflict accuracy drops from 0.589 under chance-like cue precision to 0.005 under target-perfect texture. In CIFAR-10 frozen probes, conflict accuracy drops from 0.569 to 0.114, while texture choice rises from 0.049 to 0.855; this ordering persists across texture-overlay strengths alpha in {0.15,0.25,0.35,0.50}. End-to-end CIFAR-10 training shows that low early cue precision improves pre-target conflict behavior, but shortcut-rich fine-tuning can rapidly overwrite this benefit. Cue decorrelation must therefore be maintained during downstream adaptation rather than treated as a one-time inoculation.