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Found 7 papers, 2 papers with code
Cross-Training with Multi-View Knowledge Fusion for Heterogenous Federated Learning
Federated learning benefits from cross-training strategies, which enables models to train on data from distinct sources to improve the generalization capability.
Relation Modeling and Distillation for Learning with Noisy Labels
The relation-guided representation learning (RGRL) module utilizes inter-sample relation learned from the RM module to calibrate the representation distribution for noisy samples, which is capable of improving the generalization of the model in the inference phase.
Comparative Study of Neighbor-based Methods for Local Outlier Detection
To this end, this paper studies the neighbor in the existing outlier detection algorithms and a taxonomy is introduced, which uses the three-level components of information, neighbor and methodology to define hybrid methods.
Class-level Structural Relation Modelling and Smoothing for Visual Representation Learning
Representation learning for images has been advanced by recent progress in more complex neural models such as the Vision Transformers and new learning theories such as the structural causal models.
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data
To address this issue, this paper presents a cross-silo prototypical calibration method (FedCSPC), which takes additional prototype information from the clients to learn a unified feature space on the server side.
Cross-Modal Content Inference and Feature Enrichment for Cold-Start Recommendation
Multimedia recommendation aims to fuse the multi-modal information of items for feature enrichment to improve the recommendation performance.
Meta-Causal Feature Learning for Out-of-Distribution Generalization
Causal inference has become a powerful tool to handle the out-of-distribution (OOD) generalization problem, which aims to extract the invariant features.
