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Found 3 papers, 2 papers with code
Category-wise Fine-Tuning: Resisting Incorrect Pseudo-Labels in Multi-Label Image Classification with Partial Labels
Large-scale image datasets are often partially labeled, where only a few categories' labels are known for each image.
Distance Guided Generative Adversarial Network for Explainable Binary Classifications
Experimental results show that DisGAN consistently outperforms the GAN-based augmentation methods with explainable binary classification.
A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classification
Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets.
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