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Found 5 papers, 2 papers with code
Catching Image Retrieval Generalization
The concepts of overfitting and generalization are vital for evaluating machine learning models.
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and Accuracy
Deep ensembles are capable of achieving state-of-the-art results in classification and out-of-distribution (OOD) detection.
Deep Image Retrieval is not Robust to Label Noise
Large-scale datasets are essential for the success of deep learning in image retrieval.
EXACT: How to Train Your Accuracy
Classification tasks are usually evaluated in terms of accuracy.
Ranked #2 on
Image Classification
on SVHN
(Percentage correct metric)
Probabilistic Embeddings Revisited
We thus provide a new confidence evaluation benchmark and establish a baseline for future confidence prediction research.
