The paper introduces a continual learning framework combining synthetic sketch generation and trusted sample replay to enable a single model to perform multiple sketch biometric identification tasks.
Deep learning face attributes in the wild,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MAFL uses adversarial training to suppress pattern and content biases, guiding models to learn shared generative features for better cross-model generalization in detecting AI images.
citing papers explorer
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Bridging Data Trials and Task Barriers: A Unified Framework for Sketch Biometric Identification
The paper introduces a continual learning framework combining synthetic sketch generation and trusted sample replay to enable a single model to perform multiple sketch biometric identification tasks.
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Combating Pattern and Content Bias: Adversarial Feature Learning for Generalized AI-Generated Image Detection
MAFL uses adversarial training to suppress pattern and content biases, guiding models to learn shared generative features for better cross-model generalization in detecting AI images.