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Found 9 papers, 6 papers with code
PACOL: Poisoning Attacks Against Continual Learners
In this work, we demonstrate that continual learning systems can be manipulated by malicious misinformation and present a new category of data poisoning attacks specific for continual learners, which we refer to as {\em Poisoning Attacks Against Continual Learners} (PACOL).
Knowledge Distillation Under Ideal Joint Classifier Assumption
Within this context, softmax regression representation learning serves as a widely embraced approach, leveraging a pre-established teacher network to guide the learning process of a diminutive student network.
A Maximum Log-Likelihood Method for Imbalanced Few-Shot Learning Tasks
In this work, we investigate the distributions of trained few-shot features and demonstrate that they can be roughly approximated as exponential distributions.
Shadows Aren't So Dangerous After All: A Fast and Robust Defense Against Shadow-Based Adversarial Attacks
Robust classification is essential in tasks like autonomous vehicle sign recognition, where the downsides of misclassification can be grave.
DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removal
Significance: This study is one of the first to extend the conditional diffusion-based generative model for ECG noise removal, and the DeScoD-ECG has the potential to be widely used in biomedical applications.
ProtoShotXAI: Using Prototypical Few-Shot Architecture for Explainable AI
A user explores the manifold by perturbing the input features of a query sample and recording the response for a subset of exemplars from any class.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Adversarial Filters for Secure Modulation Classification
This paper considers the problem of secure modulation classification, where a transmitter (Alice) wants to maximize MC accuracy at a legitimate receiver (Bob) while minimizing MC accuracy at an eavesdropper (Eve).
Edge-Guided Occlusion Fading Reduction for a Light-Weighted Self-Supervised Monocular Depth Estimation
In this paper, we propose a novel Edge-Guided post-processing to reduce the occlusion fading issue for self-supervised monocular depth estimation.
Hellinger distance based drift detection for nonstationary environments
Most machine learning algorithms, including many online learners, assume that the data distribution to be learned is fixed.
