A single and static dataflow may lead to a 50. 25% performance loss for GEMMs of different shapes in LLM inference.
Inspired by the success of contrastive learning used in some high-level computer vision tasks, we bring in this idea to the low-level denoising task.
This allows the camera to capture images with shallow depth-of-field, in which only a small area of the image is in sharp focus, while the rest of the image is blurred.
In the Gleason grading task, FACL attained a Kappa score of 0. 8463, surpassing the average Kappa score of 0. 7379 from six centers.
In the first stage, we propose a novel algorithm called polar decomposition-based orthogonal initialization (PDOI) to find a good initialization for the orthogonal optimization.
Specifically, we train the supernet with a large sharing extent (an easier curriculum) at the beginning and gradually decrease the sharing extent of the supernet (a harder curriculum).
More importantly, we apply the Gini coefficient and validation accuracy of clients in each communication round to construct a reward function for the reinforcement learning.
no code implementations • 6 May 2022 • Yang Liu, Ersi Zhang, Lulu Xu, Chufan Xiao, Xiaoyun Zhong, Lijin Lian, Fang Li, Bin Jiang, Yuhan Dong, Lan Ma, Qiming Huang, Ming Xu, Yongbing Zhang, Dongmei Yu, Chenggang Yan, Peiwu Qin
Deep learning techniques have shown great potential in medical image processing, particularly through accurate and reliable image segmentation on magnetic resonance imaging (MRI) scans or computed tomography (CT) scans, which allow the localization and diagnosis of lesions.
Although testing on the CAR-T cells dataset, a decent performance is observed, which is attributed to the limited size of the dataset.