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Found 8 papers, 6 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.
Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries
A large body of continual learning (CL) methods, however, assumes data streams with clean labels, and online learning scenarios under noisy data streams are yet underexplored.
Hypergraph-Induced Semantic Tuplet Loss for Deep Metric Learning
In this paper, we propose Hypergraph-Induced Semantic Tuplet (HIST) loss for deep metric learning that leverages the multilateral semantic relations of multiple samples to multiple classes via hypergraph modeling.
The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classification
The problem of class imbalanced data is that the generalization performance of the classifier deteriorates due to the lack of data from minority classes.
Ranked #20 on
Long-tail Learning
on ImageNet-LT
Influence-Balanced Loss for Imbalanced Visual Classification
In this paper, we propose a balancing training method to address problems in imbalanced data learning.
Ranked #46 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Chameleon Sampling: Diverse and Pure Example Selection for Online Continual Learning with Noisy Labels
Specifically, we argue the importance of both diversity and purity of examples in the episodic memory of continual learning models.
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.
