Sep 19, 2019 · We introduce the cross-batch reference (CBR), a novel training mechanism that enables the optimization of deep networks with a retrieval criterion.

By combining the intra-batch classification and inter-batch cross-reference losses, the learned features are effective for both classification and retrieval ...

The cross-batch reference (CBR) is introduced, a novel training mechanism that enables the optimization of deep networks with a retrieval criterion and ...

This interbatch communication is implemented as a cross-batch retrieval process in which the networks are trained to maximize the mean average precision (mAP) ...

ABSTRACT. Learning feature representations for image retrieval is essen- tial to multimedia search and mining applications. Recently, deep convolutional ...

Cross-batch Reference Learning for Deep Classification and Retrieval · Introduction · Citing the CBR · Requirements · Train a network with CBR on CIFAR-10 · Evaluate ...

Dec 14, 2019 · We propose a cross-batch memory (XBM) mechanism that memorizes the embeddings of past iterations, allowing the model to collect sufficient hard negative pairs.

Deep metric learning (DML) aims to learn an embedding space where instances from the same class are encouraged to be closer than those from different classes.

Dec 23, 2022 · Cross-batch negatives technique is used during training of model on multiple GPUs. First, passage embeddings are computed within each single ...

Sep 12, 2024 · ComBat harmonization can be used to effectively reduce batch effects in deep learning-derived histology features.