Face recognition (FR) technology plays a crucial role in various applications, but its vulnerability to adversarial attacks poses significant security concerns.
RADAP employs innovative techniques, such as FCutout and F-patch, which use Fourier space sampling masks to improve the occlusion robustness of the FR model and the performance of the patch segmenter.
The investigation of the similarity between artists and music is crucial in music retrieval and recommendation, and addressing the challenge of the long-tail phenomenon is increasingly important.
Data augmentation has been widely used in low-resource NER tasks to tackle the problem of data sparsity.
Many GCL methods with automated data augmentation face the risk of insufficient information as they fail to preserve the essential information necessary for the downstream task.
The experimental results on various datasets and CNN models verify that the proposed method outperforms other previous data augmentation methods in image classification tasks.
In this paper, we propose a new data processing and training method, called AugRmixAT, which can simultaneously improve the generalization ability and multiple robustness of neural network models.
Furthermore, we propose a random meta-optimization strategy for ensembling several pre-trained face models to generate more general adversarial masks.
From the perspective of this framework, we review those subtasks and give a unified interpretation of various scenarios.
Data augmentation plays a crucial role in enhancing the robustness and performance of machine learning models across various domains.
By improving the quantity and diversity of training data, data augmentation has become an inevitable part of deep learning model training with image data.
A special branch of adversarial examples, namely sparse adversarial examples, can fool the target DNNs by perturbing only a few pixels.
We perform extensive experiments to prove that pruning based on the influence function using the idea of ensemble learning will be much more effective than just focusing on error reconstruction.
With the development of the deep network and the release for a series of large scale datasets for single object tracking, siamese networks have been proposed and perform better than most of the traditional methods.
Inspired by the elastic collision model in physics, we present a general structure which can be integrated into the existing CNNs to improve their performance.
So we propose an exploratory architecture referred to Temporal Convolutional Attention-based Network (TCAN) which combines temporal convolutional network and attention mechanism.
PIGAT introduces the attention mechanism to consider the importance of each interacted user/item to both the user and the item, which captures user interests, item attractions and their influence on the recommendation context.
In this paper, it is the first time to discuss the difficulty without support of old classes in class incremental learning, which is called as softmax suppression problem.