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Found 7 papers, 1 papers with code
Exploring Learning Complexity for Downstream Data Pruning
In this paper, we propose to treat the learning complexity (LC) as the scoring function for classification and regression tasks.
DOS: Diverse Outlier Sampling for Out-of-Distribution Detection
Modern neural networks are known to give overconfident prediction for out-of-distribution inputs when deployed in the open world.
MixBoost: Improving the Robustness of Deep Neural Networks by Boosting Data Augmentation
Based on the analysis of the internal mechanisms, we develop a mask-based boosting method for data augmentation that comprehensively improves several robustness measures of AI models and beats state-of-the-art data augmentation approaches.
Explanation-based Counterfactual Retraining(XCR): A Calibration Method for Black-box Models
With the rapid development of eXplainable Artificial Intelligence (XAI), a long line of past work has shown concerns about the Out-of-Distribution (OOD) problem in perturbation-based post-hoc XAI models and explanations are socially misaligned.
READ: Aggregating Reconstruction Error into Out-of-distribution Detection
We propose a novel method, READ (Reconstruction Error Aggregated Detector), to unify inconsistencies from classifier and autoencoder.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise
We show that our method successfully alleviates the damage of both label noise and confirmation bias.
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Frontoparietal Connectivity Neurofeedback Training for Promotion of Working Memory: An fNIRS Study in Healthy Male Participants
Neurofeedback cognitive training is a promising tool used to promote cognitive functions effectively and efficiently.
