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Found 13 papers, 3 papers with code
Beyond Finite Data: Towards Data-free Out-of-distribution Generalization via Extrapolation
Starting with the class of interest, we query the LLMs to extract relevant knowledge for these novel domains.
Understanding Adversarial Transferability in Federated Learning
We investigate the robustness and security issues from a novel and practical setting: a group of malicious clients has impacted the model during training by disguising their identities and acting as benign clients, and only revealing their adversary position after the training to conduct transferable adversarial attacks with their data, which is usually a subset of the data that FL system is trained with.
Diverse Cotraining Makes Strong Semi-Supervised Segmentor
Deep co-training has been introduced to semi-supervised segmentation and achieves impressive results, yet few studies have explored the working mechanism behind it.
Multi-metrics adaptively identifies backdoors in Federated learning
To evaluate the effectiveness of our approach, we conduct comprehensive experiments on different datasets under various attack settings, where our method achieves the best defensive performance.
Try to Avoid Attacks: A Federated Data Sanitization Defense for Healthcare IoMT Systems
This paper introduces a Federated Data Sanitization Defense, a novel approach to protect the system from data poisoning attacks.
ConvNeXt Based Neural Network for Audio Anti-Spoofing
With the rapid development of speech conversion and speech synthesis algorithms, automatic speaker verification (ASV) systems are vulnerable to spoofing attacks.
Inference from Selectively Disclosed Data
We consider the disclosure problem of a sender with a large data set of hard evidence who wants to persuade a receiver to take higher actions.
ASE: Anomaly Scoring Based Ensemble Learning for Imbalanced Datasets
And we calculate the weights of base classifiers trained by the subsets according to the classification result of the anomaly detection model and the statistics of the subspaces.
Wallpaper Texture Generation and Style Transfer Based on Multi-label Semantics
Textures contain a wealth of image information and are widely used in various fields such as computer graphics and computer vision.
More than Encoder: Introducing Transformer Decoder to Upsample
The local and detailed feature from the shallower layer such as boundary and tissue texture is particularly more important in medical segmentation compared with natural image segmentation.
A Reputation for Honesty
We analyze situations in which players build reputations for honesty rather than for playing particular actions.
Construction and Application of Teaching System Based on Crowdsourcing Knowledge Graph
Through the combination of crowdsourcing knowledge graph and teaching system, research methods to generate knowledge graph and its applications.
Perception Driven Texture Generation
In this paper, we propose a joint deep network model that combines adversarial training and perceptual feature regression for texture generation, while only random noise and user-defined perceptual attributes are required as input.
