The field of domain generalization and representation learning is rapidly advancing, with a focus on developing methods that can learn robust and generalizable representations across different domains and tasks. Recent research has explored the use of contrastive learning, hyperbolic representations, and causal models to improve domain generalization performance. Additionally, there is a growing interest in developing methods that can learn from limited data and adapt to new domains and tasks. Notably, the use of brain-inspired models and configurations has shown promise in improving early cognitive categorization and novelty detection. Overall, the field is moving towards developing more robust and generalizable models that can adapt to a wide range of domains and tasks. Noteworthy papers include: Hypergraph Contrastive Sensor Fusion for Multimodal Fault Diagnosis in Induction Motors, which proposes a unified framework for robust fault diagnosis, and Humanoid-inspired Causal Representation Learning for Domain Generalization, which introduces a novel causal framework inspired by human intelligence.
Advances in Domain Generalization and Representation Learning
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A solution to generalized learning from small training sets found in everyday infant experiences
Hypergraph Contrastive Sensor Fusion for Multimodal Fault Diagnosis in Induction Motors
Global-focal Adaptation with Information Separation for Noise-robust Transfer Fault Diagnosis
Toward General Digraph Contrastive Learning: A Dual Spatial Perspective
Humanoid-inspired Causal Representation Learning for Domain Generalization
Instance-Aware Pseudo-Labeling and Class-Focused Contrastive Learning for Weakly Supervised Domain Adaptive Segmentation of Electron Microscopy
Connecting Domains and Contrasting Samples: A Ladder for Domain Generalization
HIDISC: A Hyperbolic Framework for Domain Generalization with Generalized Category Discovery
DAMSDAN: Distribution-Aware Multi-Source Domain Adaptation Network for Cross-Domain EEG-based Emotion Recognition
Brain-Inspired Perspective on Configurations: Unsupervised Similarity and Early Cognition
Mixing Configurations for Downstream Prediction
Towards Single-Source Domain Generalized Object Detection via Causal Visual Prompts
Unsupervised Domain Adaptation via Similarity-based Prototypes for Cross-Modality Segmentation