Extreme value theory and GANs to generate compound coastal hazards (wind speed + sea level pressure) from ERA5 reanalysis data over the Bay of Bengal.
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Updated
Jun 10, 2024 - Jupyter Notebook
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.
Extreme value theory and GANs to generate compound coastal hazards (wind speed + sea level pressure) from ERA5 reanalysis data over the Bay of Bengal.
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.
HyMPS will be a platform-indipendent software suite for advanced audio/video contents production.
(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
This is part of my dissertation thesis. I'm trying to denoise historical hungarian folk-musical recordings based on Google's article: https://arxiv.org/pdf/2008.02027
This GitHub repository showcases my bachelor thesis which is focused on exploring the application and comparison of various deep generative models for synthetic image augmentation in manufacturing domain.
Main folder. Material related to my books on synthetic data and generative AI. Also contains documents blending components from several folders, or covering topics spanning across multiple folders..
Just some notebooks I wrote to research some fun stuff in hobby time
A machine learning library for detecting anomalies in signals.
SaaS app to extract and generate color themes.
Repo for all the SRIP 2024 work at CVIG Lab IITGN under Prof. Shanmuganathan Raman
Detecting and dissecting anomalous anatomic regions in spatial transcriptomics with STANDS
Synthetic data generation for tabular data
Released June 10, 2014