Normalization techniques are essential for accelerating the training and improving the
generalization of deep neural networks (DNNs), and have successfully been used in various …

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the …

Diffusion models currently dominate the field of data-driven image synthesis with their
unparalleled scaling to large datasets. In this paper we identify and rectify several causes for …

There is a growing discrepancy in computer vision between large-scale models that achieve
state-of-the-art performance and models that are affordable in practical applications. In this …

Transfer of pre-trained representations improves sample efficiency and simplifies
hyperparameter tuning when training deep neural networks for vision. We revisit the …

COVID-19 has tremendously impacted patients and medical systems globally. Computed
tomography images can effectively complement the reverse transcription-polymerase chain …

We present MMDetection, an object detection toolbox that contains a rich set of object
detection and instance segmentation methods as well as related components and modules …

Efficient on-device learning requires a small memory footprint at training time to fit the tight
memory constraint. Existing work solves this problem by reducing the number of trainable …

Self-supervised learning provides a promising path towards eliminating the need for costly
label information in representation learning on graphs. However, to achieve state-of-the-art …