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Found 7 papers, 3 papers with code
Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction
In this paper, we present a method to learn a visual representation adapted for e-commerce products.
Addressing Failure Prediction by Learning Model Confidence
In this paper, we propose a new target criterion for model confidence, corresponding to the True Class Probability (TCP).
Addressing Failure Detection by Learning Model Confidence
In this paper, we propose a new target criterion for model confidence, corresponding to the True Class Probability (TCP).
Confidence Estimation via Auxiliary Models
In this paper, we introduce a novel target criterion for model confidence, namely the true class probability (TCP).
Effective Uncertainty Estimation with Evidential Models for Open-World Recognition
A crucial property of KLoS is to be a class-wise divergence measure built from in-distribution samples and to not require OOD training data, in contrast to current second-order uncertainty measures.
Take One Gram of Neural Features, Get Enhanced Group Robustness
Predictive performance of machine learning models trained with empirical risk minimization (ERM) can degrade considerably under distribution shifts.
Robust Deep Learning for Autonomous Driving
The last decade's research in artificial intelligence had a significant impact on the advance of autonomous driving.
