Per-sample random augmentation plus unimodal fine-tuning of pretrained layers improves late-fusion multimodal classification on FPU23 fetal ultrasound (up to 96.9% head detection) and reaches 92.63% on UPMC Food-101, but the two recipe components are not ablated separately.
The effectiveness of data augmentation in image classification using deep learning,
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Batch Augmentation with Unimodal Fine-tuning for Multimodal Learning
Per-sample random augmentation plus unimodal fine-tuning of pretrained layers improves late-fusion multimodal classification on FPU23 fetal ultrasound (up to 96.9% head detection) and reaches 92.63% on UPMC Food-101, but the two recipe components are not ablated separately.