画像分類タスクにおける単純性バイアスの現代的考察
Xiaoguang Chang1, Teng Wang2, Changyin Sun2
1School of Cyber Science and Engineering, Southeast University, Nanjing, China.
まとめ
ニューラルネットワークにおける単純性バイアス（SB）は一般化を助ける。本研究では、CLIPモデルにおけるSBの周波数依存指標を導入し、様々な画像分類タスクへの影響を明らかにし、最適なモデル設計を導く。
科学分野:
- 人工知能; 機械学習; コンピュータビジョン
背景:
- 単純性バイアス（SB）はニューラルネットワークの一般化に重要であるが、その実世界への影響は不明確である。; 既存の複雑さ指標は、モデルの表現力との混同により、SBを不正確に評価する可能性がある。
研究 の 目的:
- CLIPモデルにおけるSBとその画像分類一般化への影響を体系的に調査する。; SBを定量化するための新しい周波数依存指標を提案する。; 様々な一般化タスクにおけるSB変調のトレードオフを評価する。
主な方法:
- 複雑さ指標の理論的分析。; 周波数制限入力空間における1次元補間を用いた周波数依存SB指標の開発。; OOD、画像劣化、敵対的タスクにおけるゼロショットおよびファインチューニング評価のためのCLIPモデルにおけるSB変調。
主要な成果:
- 提案された指標は、SBがモデルの感度を低周波成分にシフトさせることを明らかにし、一般化の改善を説明する。; より強いSBは分布外（OOD）一般化を強化する。; より強いSBは敵対的頑健性の低下と相関する。
結論:
- SBはCLIPモデルの一般化能力において重要な役割を果たす。; 周波数依存指標は、SBメカニズムに関するより深い洞察を提供する。; SBのバランスをとることは、特にOODと敵対的頑健性の間の、様々な一般化シナリオにおけるモデルパフォーマンスを最適化するために重要である。
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