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Found 21 papers, 15 papers with code
Safe and Robust Watermark Injection with a Single OoD Image
Training a high-performance deep neural network requires large amounts of data and computational resources.
Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection
We propose to take advantage of such heterogeneity and turn the curse into a blessing that facilitates OoD detection in FL.
Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork
As a result, both the stem and the classification head in the final network are hardly affected by backdoor training samples.
How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts
Increasing concerns have been raised on deep learning fairness in recent years.
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition
However, in real-world applications, it is common for the training sets to have long-tailed distributions.
Removing Batch Normalization Boosts Adversarial Training
In addition, NoFrost achieves a $23. 56\%$ adversarial robustness against PGD attack, which improves the $13. 57\%$ robustness in BN-based AT.
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization
In this paper, we propose a novel Split-Mix FL strategy for heterogeneous participants that, once training is done, provides in-situ customization of model sizes and robustness.
AugMax: Adversarial Composition of Random Augmentations for Robust Training
Diversity and hardness are two complementary dimensions of data augmentation to achieve robustness.
Equalized Robustness: Towards Sustainable Fairness Under Distributional Shifts
In this paper, we first propose a new fairness goal, termed Equalized Robustness (ER), to impose fair model robustness against unseen distribution shifts across majority and minority groups.
Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated Learning
In this paper, we study a novel FL strategy: propagating adversarial robustness from rich-resource users that can afford AT, to those with poor resources that cannot afford it, during federated learning.
Taxonomy of Machine Learning Safety: A Survey and Primer
The open-world deployment of Machine Learning (ML) algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities such as interpretability, verifiability, and performance limitations.
Troubleshooting Blind Image Quality Models in the Wild
Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics.
Efficiently Troubleshooting Image Segmentation Models with Human-In-The-Loop
Image segmentation lays the foundation for many high-stakes vision applications such as autonomous driving and medical image analysis.
UMEC: Unified model and embedding compression for efficient recommendation systems
The recommendation system (RS) plays an important role in the content recommendation and retrieval scenarios.
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free
The trained model could be adjusted among different standard and robust accuracies "for free" at testing time.
GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
Generative adversarial networks (GANs) have gained increasing popularity in various computer vision applications, and recently start to be deployed to resource-constrained mobile devices.
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
Inspired by the recent success of AutoML in deep compression, we introduce AutoML to GAN compression and develop an AutoGAN-Distiller (AGD) framework.
I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively
On the other hand, the trained classifiers have traditionally been evaluated on small and fixed sets of test images, which are deemed to be extremely sparsely distributed in the space of all natural images.
Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference
Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images) (Tsipras et al., 2019).
Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset
We first discuss an innovative heuristic of cross-dataset training and evaluation, enabling the use of multiple single-task datasets (one with target task labels and the other with privacy labels) in our problem.
Model Compression with Adversarial Robustness: A Unified Optimization Framework
Deep model compression has been extensively studied, and state-of-the-art methods can now achieve high compression ratios with minimal accuracy loss.
