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Found 6 papers, 3 papers with code
Transferability Metrics for Object Detection
In our experiments, we compare TLogME to state-of-the-art metrics in the estimation of transfer performance of the Faster-RCNN object detector.
Towards Clear Expectations for Uncertainty Estimation
If Uncertainty Quantification (UQ) is crucial to achieve trustworthy Machine Learning (ML), most UQ methods suffer from disparate and inconsistent evaluation protocols.
Performance Prediction Under Dataset Shift
ML models deployed in production often have to face unknown domain changes, fundamentally different from their training settings.
Ensembling Shift Detectors: an Extensive Empirical Evaluation
The term dataset shift refers to the situation where the data used to train a machine learning model is different from where the model operates.
Who wrote this book? A challenge for e-commerce
Modern e-commerce catalogs contain millions of references, associated with textual and visual information that is of paramount importance for the products to be found via search or browsing.
Robustness of Rotation-Equivariant Networks to Adversarial Perturbations
Deep neural networks have been shown to be vulnerable to adversarial examples: very small perturbations of the input having a dramatic impact on the predictions.
