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Found 4 papers, 3 papers with code
Confidence-Based Feature Imputation for Graphs with Partially Known Features
To overcome this limitation, we introduce a novel concept of channel-wise confidence in a node feature, which is assigned to each imputed channel feature of a node for reflecting certainty of the imputation.
RoCOCO: Robustness Benchmark of MS-COCO to Stress-test Image-Text Matching Models
In this paper, we propose a robustness benchmark for image-text matching models to assess their vulnerabilities.
Influential Rank: A New Perspective of Post-training for Robust Model against Noisy Labels
Deep neural network can easily overfit to even noisy labels due to its high capacity, which degrades the generalization performance of a model.
Class-Attentive Diffusion Network for Semi-Supervised Classification
In contrast to the existing diffusion methods with a transition matrix determined solely by the graph structure, CAD considers both the node features and the graph structure with the design of our class-attentive transition matrix that utilizes a classifier.
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