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Found 17 papers, 1 papers with code
Towards Adversarially Robust Continual Learning
Recent studies show that models trained by continual learning can achieve the comparable performances as the standard supervised learning and the learning flexibility of continual learning models enables their wide applications in the real world.
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends
Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field.
Bayesian Evidential Learning for Few-Shot Classification
Few-Shot Classification(FSC) aims to generalize from base classes to novel classes given very limited labeled samples, which is an important step on the path toward human-like machine learning.
Memory-Based Label-Text Tuning for Few-Shot Class-Incremental Learning
The difficulties are that training on a sequence of limited data from new tasks leads to severe overfitting issues and causes the well-known catastrophic forgetting problem.
Geometric Anchor Correspondence Mining With Uncertainty Modeling for Universal Domain Adaptation
Therefore, in this paper, we propose a Geometric anchor-guided Adversarial and conTrastive learning framework with uncErtainty modeling called GATE to alleviate these issues.
Ranked #5 on
Universal Domain Adaptation
on Office-Home
Towards Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-based Method
Through extensive experiments, AI-GAN achieves high attack success rates, outperforming existing methods, and reduces generation time significantly.
Adversarial Purification through Representation Disentanglement
Deep learning models are vulnerable to adversarial examples and make incomprehensible mistakes, which puts a threat on their real-world deployment.
Neighborhood Consensus Contrastive Learning for Backward-Compatible Representation
Therefore, backward-compatible representation is proposed to enable "new" features to be compared with "old" features directly, which means that the database is active when there are both "new" and "old" features in it.
Inconspicuous Adversarial Patches for Fooling Image Recognition Systems on Mobile Devices
The patches are encouraged to be consistent with the background images with adversarial training while preserving strong attack abilities.
Recent Advances in Adversarial Training for Adversarial Robustness
Adversarial training is one of the most effective approaches defending against adversarial examples for deep learning models.
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks
Adversarial examples are inevitable on the road of pervasive applications of deep neural networks (DNN).
Generating Adversarial yet Inconspicuous Patches with a Single Image
Through extensive experiments, our ap-proach shows strong attacking ability in both the white-box and black-box setting.
Feature Distillation With Guided Adversarial Contrastive Learning
In this paper, we propose a novel approach called Guided Adversarial Contrastive Distillation (GACD), to effectively transfer adversarial robustness from teacher to student with features.
AI-GAN: Attack-Inspired Generation of Adversarial Examples
Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding imperceptible perturbations to inputs.
Reviewing and Improving the Gaussian Mechanism for Differential Privacy
Although both classical Gaussian mechanisms [1, 2] assume $0 < \epsilon \leq 1$, our review finds that many studies in the literature have used the classical Gaussian mechanisms under values of $\epsilon$ and $\delta$ where the added noise amounts of [1, 2] do not achieve $(\epsilon,\delta)$-DP.
Targeting Ultimate Accuracy: Face Recognition via Deep Embedding
Face Recognition has been studied for many decades.
