Filter or Compensate: Towards Invariant Representation from Distribution Shift for Anomaly Detection

Documenti analoghi

• PracticalDG: Perturbation Distillation on Vision-Language Models for Hybrid Domain Generalization
  di: Chen, Zining, et al.
  Pubblicazione: (2024)
• Data-Efficient Generalization for Zero-shot Composed Image Retrieval
  di: Chen, Zining, et al.
  Pubblicazione: (2025)
• Camera-Invariant Meta-Learning Network for Single-Camera-Training Person Re-identification
  di: Pei, Jiangbo, et al.
  Pubblicazione: (2024)
• Robust Distribution Alignment for Industrial Anomaly Detection under Distribution Shift
  di: Liao, Jingyi, et al.
  Pubblicazione: (2025)
• TriDP-PTM: a three-stage distortion-perception tradeoff guides the pre-training model for radar cardiac sensing
  di: Li, Jinye, et al.
  Pubblicazione: (2026)