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Found 6 papers, 2 papers with code
PC-Adapter: Topology-Aware Adapter for Efficient Domain Adaption on Point Clouds with Rectified Pseudo-label
Understanding point clouds captured from the real-world is challenging due to shifts in data distribution caused by varying object scales, sensor angles, and self-occlusion.
SGEM: Test-Time Adaptation for Automatic Speech Recognition via Sequential-Level Generalized Entropy Minimization
Automatic speech recognition (ASR) models are frequently exposed to data distribution shifts in many real-world scenarios, leading to erroneous predictions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification
Learning unbiased node representations under class-imbalanced graph data is challenging due to interactions between adjacent nodes.
Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing
However, they now suffer from lack of sample diversification as they always deterministically select regions with maximum saliency, injecting bias into the augmented data.
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation
Graph-structured datasets usually have irregular graph sizes and connectivities, rendering the use of recent data augmentation techniques, such as Mixup, difficult.
GraphENS: Neighbor-Aware Ego Network Synthesis for Class-Imbalanced Node Classification
In many real-world node classification scenarios, nodes are highly class-imbalanced, where graph neural networks (GNNs) can be readily biased to major class instances.
