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Found 13 papers, 7 papers with code
Unlearnable Examples For Time Series
In this work, we introduce the first UE generation method to protect time series data from unauthorized training by deep learning models.
LDReg: Local Dimensionality Regularized Self-Supervised Learning
Representations learned via self-supervised learning (SSL) can be susceptible to dimensional collapse, where the learned representation subspace is of extremely low dimensionality and thus fails to represent the full data distribution and modalities.
End-to-End Anti-Backdoor Learning on Images and Time Series
Backdoor attacks present a substantial security concern for deep learning models, especially those utilized in applications critical to safety and security.
Backdoor Attacks on Time Series: A Generative Approach
We find that, compared to images, it can be more challenging to achieve the two goals on time series.
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks
Specifically, we make the following key observations: 1) more parameters (higher model capacity) does not necessarily help adversarial robustness; 2) reducing capacity at the last stage (the last group of blocks) of the network can actually improve adversarial robustness; and 3) under the same parameter budget, there exists an optimal architectural configuration for adversarial robustness.
$\alpha$-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression
Bounding box (bbox) regression is a fundamental task in computer vision.
Dual Head Adversarial Training
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples/attacks, raising concerns about their reliability in safety-critical applications.
Unlearnable Examples: Making Personal Data Unexploitable
This paper raises the question: \emph{can data be made unlearnable for deep learning models?}
MMF: Attribute Interpretable Collaborative Filtering
Collaborative filtering is one of the most popular techniques in designing recommendation systems, and its most representative model, matrix factorization, has been wildly used by researchers and the industry.
FCC-GAN: A Fully Connected and Convolutional Net Architecture for GANs
Generative Adversarial Networks (GANs) are a powerful class of generative models.
Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality
In this paper, we demonstrate that an intrinsic dimensional characterization of the data space learned by a GAN model leads to an effective evaluation metric for GAN quality.
Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
Learning nonlinear dynamics from diffusion data is a challenging problem since the individuals observed may be different at different time points, generally following an aggregate behaviour.
Unsupervised Adversarial Anomaly Detection using One-Class Support Vector Machines
Anomaly detection discovers regular patterns in unlabeled data and identifies the non-conforming data points, which in some cases are the result of malicious attacks by adversaries.
