Chaotic maps act as augmentations in contrastive pre-training to learn topologically robust texture features, outperforming SOTA on six benchmarks when combined with attention-based fusion.
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Fisher vector encoding integrated into CNN-ViT hybrids outperforms benchmarks on MedMNIST datasets and matches literature results on other medical image sets.
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Chaotic Contrastive Learning for Robust Texture Classification
Chaotic maps act as augmentations in contrastive pre-training to learn topologically robust texture features, outperforming SOTA on six benchmarks when combined with attention-based fusion.
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Deep neural networks with Fisher vector encoding for medical image classification
Fisher vector encoding integrated into CNN-ViT hybrids outperforms benchmarks on MedMNIST datasets and matches literature results on other medical image sets.