… (a) We present an online data mixing approach based on an adversarial learning policy,
trained end-to-end to automatically produce mixed samples. (b) We propose an adversarial …

… parameters in transformation adversarial networks for data augmentation (TANDA) [39…
Among all mixing-based AutoDA methods, AutoMixup and AlignMixup perform the best on …

… the Adversarial Mixing Policy (AMP), which adds an adversarial perturbation to the mixing
ratio to relax the linear restrictions. AOM combines mixup methods and PGD adversarial …

… AdAutoMix [51] further improves AutoMix by proposing adversarial training to train the
generator and obtain “hard mixed sample" to train the model. Manifold Mixup [64] and AlignMix [63…

… Then to enhance resilience against adversarial attacks and improves safety measures a
randomly … fine-grained classification fine-tuning data scarcity and adversarial robustness. …

… combination schemes with adversarial learning-based data augmentation [31] [32]. … He,
”Attngan: Finegrained text to image generation with attentional generative adversarial networks,” …

… adversarial perturbations. We have conducted extensive experiments on two public vein
datasets under different adversarial … a wide variety of adversarial perturbations, allowing vein …

… , AdAutoMix[29], built upon AutoMix, has been proposed to augment the generated samples
by mixing any set of N samples and not only two; AdAutoMix also proposed adversarial …

… First, the Synthetic Minority Over-sampling Technique is used to generate samples of the
minority class, and then these samples are fed into a Generative Adversarial Network to …

Palm vein recognition has attracted recently wide attention thanks to its robust feature
representation and high accuracy. Despite advancements in the literature, however, existing …