EDL learns a transferable classification loss from unlimited synthetic data via evolutionary optimization and a ranking-consistency objective, serving as a competitive drop-in replacement for cross-entropy on CIFAR-10 with ResNet models.
Additive margin softmax for face verification
2 Pith papers cite this work. Polarity classification is still indexing.
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DeepFense supplies a unified toolkit and large-scale benchmarks showing that pre-trained front-end feature extractors drive most performance differences while top models exhibit strong biases by audio quality, speaker gender, and language.
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Distribution-Free Pretraining of Classification Losses via Evolutionary Dynamics
EDL learns a transferable classification loss from unlimited synthetic data via evolutionary optimization and a ranking-consistency objective, serving as a competitive drop-in replacement for cross-entropy on CIFAR-10 with ResNet models.
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DeepFense: A Unified, Modular, and Extensible Framework for Robust Deepfake Audio Detection
DeepFense supplies a unified toolkit and large-scale benchmarks showing that pre-trained front-end feature extractors drive most performance differences while top models exhibit strong biases by audio quality, speaker gender, and language.