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Found 6 papers, 4 papers with code
BiasAdv: Bias-Adversarial Augmentation for Model Debiasing
Our key idea is that an adversarial attack on a biased model that makes decisions based on spurious correlations may generate synthetic bias-conflicting samples, which can then be used as augmented training data for learning a debiased model.
Sample-wise Label Confidence Incorporation for Learning with Noisy Labels
Although recent studies on designing a robust objective function to label noise, known as the robust loss method, have shown promising results for learning with noisy labels, they suffer from the issue of underfitting not only noisy samples but also clean ones, leading to suboptimal model performance.
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.
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)
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.
Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-Identification
Our strategy alleviates the problem of gradient vanishing in low-level layers and robustly trains the low-level layers to fit the ReID dataset, thereby increasing the performance of ReID tasks.
