docs: documentation for ydata synthetic (#247) May 31, 2023 .releaserc.json chore(actions): move dependency management to renovatebot (#204) Nov 30, 2022 LICENSE Update LICENSE Feb 10, 2022 MANIFEST.in chore(pipelines): automatic releases and publishing (#34) ...
YData Synthetic YData-Syntheticis an open-source package developed in 2020 with the primary goal of educating users about generative models for synthetic data generation. Designed as a collection of models, it was intended for exploratory studies and educational purposes. However, it was not optimiz...
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This paper proposes a novel generative adversarial network-based synthetic data training (GAN-ST) model to generate synthetic data for training a lightweight convolutional neural network (CNN). An enhanced generator is proposed to quickly saturate and cover the colour space of the training ...
To this end, we generate MOTSynth, a large, highly diverse synthetic dataset for object detection and tracking using a rendering game engine. Our experiments show that MOTSynth can be used as a replacement for real data on tasks such as pedestrian detection, re-identification, segmentation, and...
MEMD decomposition of composite time series consisting of the original 3-channel synthetic data [X Y Z] and noise reference channels.Meng, HuHualou, Liang
private-dataset birdcled2024-models birdclef2024-additional-cleaned birdclef2024-openvino-model trending_flat See All Language Python Competition Notebook BirdCLEF 2024 License This Notebook has been released under the Apache 2.0 open source license. Continue exploring Input7 files arrow_right_alt Output...
A synthetic training data item comprising a first sequence of symbols that represent a synthetic sentence output by a simulator is received. The synthetic training data item is processed using a machine learning model, which outputs a second sequence of symbols that represent the synthetic sentence....
Our experimental results show that the proposed Rejuv3DNet and the KR model achieve 62.5% and 66.67%, respectively, on real-world data, while these techniques achieve a prediction accuracy of 75.2% and 89.5%, and 77.2% and 90.1% on our two different synthetic datasets. Our proposed techniques...
ydata-synthetic Public Synthetic data generators for tabular and time-series data Jupyter Notebook 1.5k 244 academy Public Tutorials for YData's Fabric platform Jupyter Notebook 31 7 ydata-talkdatatome Public Make your dataset talk to you. The AI assistant for data preparation. Pyth...