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Found 8 papers, 1 papers with code
Analysis of Coding Gain Due to In-Loop Reshaping
Reshaping, a point operation that alters the characteristics of signals, has been shown capable of improving the compression ratio in video coding practices.
Complementary Labels Learning with Augmented Classes
In this paper, we propose a novel problem setting called Complementary Labels Learning with Augmented Classes (CLLAC), which brings the challenge that classifiers trained by complementary labels should not only be able to classify the instances from observed classes accurately, but also recognize the instance from the Augmented Classes in the testing phase.
InfoAT: Improving Adversarial Training Using the Information Bottleneck Principle
Therefore, guaranteeing the robustness of hard examples is crucial for improving the final robustness of the model.
Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
Further, we propose Scale-Invariant (SI) adversarial defense mechanism based on the cosine angle matrix, which can be embedded into the popular adversarial defenses.
MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical Pretrained Models Against Adversarial Attack
However, the defense methods that have good effect in natural images may not be suitable for medical diagnostic tasks.
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack
Deep learning models (with neural networks) have been widely used in challenging tasks such as computer-aided disease diagnosis based on medical images.
Improving the Certified Robustness of Neural Networks via Consistency Regularization
A range of defense methods have been proposed to improve the robustness of neural networks on adversarial examples, among which provable defense methods have been demonstrated to be effective to train neural networks that are certifiably robust to the attacker.
DISCO: Influence Maximization Meets Network Embedding and Deep Learning
As the number of required samples have been recently proven to be lower bounded by a particular threshold that presets tradeoff between the accuracy and efficiency, the result quality of these traditional solutions is hard to be further improved without sacrificing efficiency.
