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Found 13 papers, 7 papers with code
An Element-Wise Weights Aggregation Method for Federated Learning
A central challenge in FL is the effective aggregation of local model weights from disparate and potentially unbalanced participating clients.
In-context Prompt Learning for Test-time Vision Recognition with Frozen Vision-language Model
To this end, we explore the concept of test-time prompt tuning (TTPT), which enables the adaptation of the CLIP model to novel downstream tasks through only one step of optimization on an unsupervised objective that involves the test sample.
A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective
In this survey paper, our research indicates that the to-learn data, the learning gradients, and the learned model at different stages all can be manipulated to initiate malicious attacks that range from undermining model performance, reconstructing private local data, and to inserting backdoors.
Steel Surface Roughness Parameter Calculations Using Lasers and Machine Learning Models
By comparing a selection of data-driven approaches, including both deep learning and non-deep learning methods, to the close-form transformation, we evaluate their potential for improving surface texture control in temper strip steel manufacturing.
Gradient Leakage Defense with Key-Lock Module for Federated Learning
Our proposed learning method is resistant to gradient leakage attacks, and the key-lock module is designed and trained to ensure that, without the private information of the key-lock module: a) reconstructing private training data from the shared gradient is infeasible; and b) the global model's inference performance is significantly compromised.
Predicting Surface Texture in Steel Manufacturing at Speed
Control of the surface texture of steel strip during the galvanizing and temper rolling processes is essential to satisfy customer requirements and is conventionally measured post-production using a stylus.
Jacobian Norm with Selective Input Gradient Regularization for Improved and Interpretable Adversarial Defense
Moreover, adversarially training a defense model in general cannot produce interpretable predictions towards the inputs with perturbations, whilst a highly interpretable robust model is required by different domain experts to understand the behaviour of a DNN.
GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learning
In this paper, we show that, in the FL system, image-based privacy data can be easily recovered in full from the shared gradient only via our proposed Generative Regression Neural Network (GRNN).
Scene Context-Aware Salient Object Detection
To our knowledge, such high-level semantic contextual information of image scenes is under-explored for saliency detection in the literature.
Learnable Gabor modulated complex-valued networks for orientation robustness
Robustness to transformation is desirable in many computer vision tasks, given that input data often exhibits pose variance.
FedBoosting: Federated Learning with Gradient Protected Boosting for Text Recognition
Typical machine learning approaches require centralized data for model training, which may not be possible where restrictions on data sharing are in place due to, for instance, privacy and gradient protection.
Graph Based Convolutional Neural Network
Signal filters take the form of spectral multipliers, applying convolution in the graph spectral domain.
From Pose to Activity: Surveying Datasets and Introducing CONVERSE
We present a review on the current state of publicly available datasets within the human action recognition community; highlighting the revival of pose based methods and recent progress of understanding person-person interaction modeling.
