STL-10 dataset is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. It is inspired by theCIFAR-10 datasetbut with some modifications.http://www.stanford.edu/~acoates//stl10/ The Street View House Numbers (SVHN) Dataset -htt...
Illustration of the self-supervised pre-training and fine-tuning procedure. In the first step, a deep learning model is pre-trained using self-supervised learning on a large unlabeled medical image dataset. In the second step, the pre-trained model is fine-tuned for a medical downstream task ...
本节说明需要数据集和任务包含一些属性来挑战当前的offline RL算法,这些属性如下: Narrow and biased data distributions: 类似于确定性策略所产生的数据对于offline RL可能造成发散。这种较窄分布的数据集可能来源于专家数据等 Undirected and multitask data: 这种数据一般来源于被动的记录,即网络上记录用户交互或自动驾...
Penn Machine Learning Benchmarks – Clean, tabular datasets Link:https://github.com/EpistasisLab/pmlb/tree/master/datasets A collection of preprocessed datasets in tabular form More appropriate for traditional machine learning rather than deep learning Public APIs – A list of public dataset API Lin...
The offline reinforcement learning (RL) problem, also referred to as batch RL, refers to the setting where a policy must be learned from a dataset of previously collected data, without additional online data collection. In supervised learning, large datasets and complex deep neural networks have ...
States of the parts such as the rotation in different rotation axis need to be recognized by a computer-vision system. 部件的状态需要由计算机视觉系统来识别,例如在不同旋转轴上的旋转。 keywords: digital twin, artificial intelligence, robotics, artificial datasets, machine learning 数字孪生,人工智能,...
This research is a continuation of some ideas presented in this blog post and is a joint collaboration between GitHub and the Deep Program Understanding group at Microsoft Research - Cambridge. We aim to provide a platform for community research on semantic code search via the following: ...
This is an example of how to run GDL with hydra in simple steps with themassachusetts buildingsdataset in thetests/data/folder, for segmentation on buildings: Clone this github repo. (geo_deep_env) $ git clone https://github.com/NRCan/geo-deep-learning.git (geo_deep_env) $cdgeo-deep-...
Deep learning (DL) has been proved to be good at capturing complex and abstract features from numerous data30. Many studies have applied the DL based super-resolution (SR) approaches for downscaling31,32,33. Among the DL approaches, UNet shows superior performance in the field of SR and has...
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