Synthetic data generation for tabular data
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Updated
Jun 13, 2024 - Python
Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset.
Synthetic data generation for tabular data
Detecting and subtyping anomalous single cells with M2ASDA
This repository contains all the machine learning and deep learning model I have implemented using various frameworks like keras, tensorflow, scikit-learn, pytorch, etc.
State-of-the-art audio codec with 90x compression factor. Supports 44.1kHz, 24kHz, and 16kHz mono/stereo audio.
HyMPS will be a platform-indipendent software suite for advanced audio/video contents production.
Detecting and dissecting anomalous anatomic regions in spatial transcriptomics with STANDS
The Super-Resolution for Renewable Resource Data (sup3r) software uses generative adversarial networks to create synthetic high-resolution wind and solar spatiotemporal data from coarse low-resolution inputs.
Extreme value theory and GANs to generate compound coastal hazards (wind speed + sea level pressure) from ERA5 reanalysis data over the Bay of Bengal.
A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps
Official Implementation for "HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach"
Code for our paper "Generative Adversarial Network with Soft-Dynamic Time Warping and Parallel Reconstruction for Energy Time Series Anomaly Detection" and its extension.
EDN-GTM Scheme for Single Image Dehazing
Benchmarking synthetic data generation methods.
PyTorch implementation (with up-to-date tooling) of the SAM / DAC algorithm
Synthetic Data Generation for mixed-type, multivariate time series.
Сustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.
Conditional GAN for generating synthetic tabular data.
(ෆ`꒳´ෆ) A Survey on Text-to-Image Generation/Synthesis.
Generation of faces, numbers and images...And Stable-Diffusion Inpainting through Segmentation through SAM and CLIP Model
🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
Released June 10, 2014