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Found 6 papers, 4 papers with code
Adversarial Reweighting for Partial Domain Adaptation
To tackle the challenge of negative domain transfer, we propose a novel Adversarial Reweighting (AR) approach that adversarially learns the weights of source domain data to align the source and target domain distributions, and the transferable deep recognition network is learned on the reweighted source domain data.
Ranked #1 on
Partial Domain Adaptation
on DomainNet
Deep Deterministic Information Bottleneck with Matrix-based Entropy Functional
We introduce the matrix-based Renyi's $\alpha$-order entropy functional to parameterize Tishby et al. information bottleneck (IB) principle with a neural network.
Measuring Dependence with Matrix-based Entropy Functional
Measuring the dependence of data plays a central role in statistics and machine learning.
Gated Information Bottleneck for Generalization in Sequential Environments
By learning minimum sufficient representations from training data, the information bottleneck (IB) approach has demonstrated its effectiveness to improve generalization in different AI applications.
Adversarial Robustness
Out of Distribution (OOD) Detection
+1
Privacy-Preserving Federated Learning on Partitioned Attributes
Real-world data is usually segmented by attributes and distributed across different parties.
INSURE: An Information Theory Inspired Disentanglement and Purification Model for Domain Generalization
Specifically, we first propose an information theory inspired loss function to ensure the disentangled class-relevant features contain sufficient class label information and the other disentangled auxiliary feature has sufficient domain information.
