1 citations · 1 across the 6 of their papers we have counts for
6 papers
Catching Image Retrieval Generalization
Maksim Zhdanov, Ivan Karpukhin
The concepts of overfitting and generalization are vital for evaluating machine learning models. In this work, we show that the popular Recall@K metric depends on the number of cla…
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and Accuracy
Stanislav Dereka, Ivan Karpukhin, Maksim Zhdanov +1
Deep ensembles are capable of achieving state-of-the-art results in classification and out-of-distribution (OOD) detection. However, their effectiveness is limited due to the homog…
Deep Image Retrieval is not Robust to Label Noise
Stanislav Dereka, Ivan Karpukhin, Sergey Kolesnikov
Large-scale datasets are essential for the success of deep learning in image retrieval. However, manual assessment errors and semi-supervised annotation techniques can lead to labe…
EXACT: How to Train Your Accuracy
Ivan Karpukhin, Stanislav Dereka, Sergey Kolesnikov
Classification tasks are usually evaluated in terms of accuracy. However, accuracy is discontinuous and cannot be directly optimized using gradient ascent. Popular methods minimize…
HiFi-VC: High Quality ASR-Based Voice Conversion
A. Kashkin, I. Karpukhin, S. Shishkin
The goal of voice conversion (VC) is to convert input voice to match the target speaker's voice while keeping text and prosody intact. VC is usually used in entertainment and speak…
Probabilistic Embeddings Revisited
Ivan Karpukhin, Stanislav Dereka, Sergey Kolesnikov
In recent years, deep metric learning and its probabilistic extensions claimed state-of-the-art results in the face verification task. Despite improvements in face verification, pr…