9 papers with code • 0 benchmarks • 0 datasets
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Non-exemplar class-incremental learning is to recognize both the old and new classes when old class samples cannot be saved.
Using this similarity measure, we propose several related algorithms for ranking data instances and performing numerosity reduction.
Given a set of hand-crafted local features, acquiring a global representation via aggregation is a promising technique to boost computational efficiency and improve task performance.
We propose a novel approach to anomaly detection called Curvature Anomaly Detection (CAD) and Kernel CAD based on the idea of polyhedron curvature.
In this work, we propose a unified framework called Latent Distribution Adjusting (LDA) with properties of latent, discriminative, adaptive, generic to improve the robustness of the FAS model by adjusting complex data distribution with multiple prototypes.