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Deep Face Detector Adaptation Without Negative Transfer or Catastrophic Forgetting

CVPR 2018 · Continual learning › Domain and prompt-based · approach: Domain-incremental learning with memory · 12 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Adapt the source detector using the residual-loss and residual-score methodFaster-RCNN · FDDBROC (discrete scores)Helped
Adapt the source detector using the residual-loss and residual-score methodCascadeCNN · FDDBROC (discrete scores)Helped
Adapt the source detector using the residual-loss and residual-score methodFaster-RCNN and CascadeCNN · FDDB, semi-supervised adaptationROCHelped
Adapt the detector with Gaussian-process score updatesSource face detectors · FDDB, unsupervised adaptationROCNo clear effect
Add the residual loss (ablation)Proposed face detector adaptation method · FDDB, supervised adaptationROCHelped
Parameterize the target classifier as the source classifier plus an offset (residual score) (ablation)Proposed face detector adaptation method · FDDB, supervised adaptationROCHelped
Fine-tune the detector on target-domain labels without the proposed safeguardsSource face detector · WIDER FACE validation set (Easy, Medium, and Hard)ROCHurt
Use residual loss and residual-score adaptation rather than ordinary fine-tuningSource face detector · WIDER FACE validation setROCHelped

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