This novel algorithm can leverage privileged information into SCN in the training stage, which provides a new method to train SCN.
Although nanorobots have been used as clinical prescriptions for work such as gastroscopy, and even photoacoustic tomography technology has been proposed to control nanorobots to deliver drugs at designated delivery points in real time, and there are cases of eliminating "superbacteria" in blood through nanorobots, most technologies are immature, either with low efficiency or low accuracy, Either it can not be mass produced, so the most effective way to treat cancer diseases at this stage is through chemotherapy and radiotherapy.
EventZoom is trained in a noise-to-noise fashion where the two ends of the network are unfiltered noisy events, enforcing noise-free event restoration.
"Pay-per-last-$N$-shares" (PPLNS) is one of the most common payout strategies used by mining pools in Proof-of-Work (PoW) cryptocurrencies.
Fairness Computer Science and Game Theory Cryptography and Security
Our analysis of the phenomenon reveals why our algorithm works.
Ranked #1 on Out-of-Distribution Detection on MS-1M vs. IJB-C
The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically.
Existing robust mixture regression methods suffer from outliers as they either conduct parameter estimation in the presence of outliers, or rely on prior knowledge of the level of outlier contamination.
To combine the distribution-level relations and instance-level relations for all examples, we construct a dual complete graph network which consists of a point graph and a distribution graph with each node standing for an example.
Ranked #1 on Few-Shot Learning on Mini-ImageNet - 1-Shot Learning
To tackle this problem, we propose an efficient attention mechanism - Pose Refine Machine (PRM) to make a trade-off between local and global representations in output features and further refine the keypoint locations.
Ranked #1 on Keypoint Detection on COCO
We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e. g., 10-150 MFLOPs).
Ranked #70 on Person Re-Identification on DukeMTMC-reID
Previous approaches for scene text detection have already achieved promising performances across various benchmarks.
Ranked #3 on Scene Text Detection on COCO-Text
Fully convolutional neural networks give accurate, per-pixel prediction for input images and have applications like semantic segmentation.
In addition, we propose balanced quantization methods for weights to further reduce performance degradation.
Recently, scene text detection has become an active research topic in computer vision and document analysis, because of its great importance and significant challenge.
Ranked #6 on Scene Text Detection on COCO-Text
We propose DoReFa-Net, a method to train convolutional neural networks that have low bitwidth weights and activations using low bitwidth parameter gradients.
In this paper, we propose and study a technique to reduce the number of parameters and computation time in convolutional neural networks.
Different from focused texts present in natural images, which are captured with user's intention and intervention, incidental texts usually exhibit much more diversity, variability and complexity, thus posing significant difficulties and challenges for scene text detection and recognition algorithms.
Recently, text detection and recognition in natural scenes are becoming increasing popular in the computer vision community as well as the document analysis community.