Repo for all the SRIP 2024 work at CVIG Lab IITGN under Prof. Shanmuganathan Raman
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Updated
Jun 8, 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.
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
Сustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.
Just some notebooks I wrote to research some fun stuff in hobby time
Benchmarking synthetic data generation methods.
Repository relating to the Coursera Course Generative Adversarial Networks
Synthetic data generation for tabular data
(ෆ`꒳´ෆ) A Survey on Text-to-Image Generation/Synthesis.
HyMPS will be a platform-indipendent software suite for advanced audio/video contents production.
An AI for Music Generation
OPVTON is the GAN model I trained that takes a clothing item, a human and generates an image with the clothing item on the human even when there's occlusion in the torso area
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.
Implementation of GigaGAN, new SOTA GAN out of Adobe. Culmination of nearly a decade of research into GANs
State-of-the-art audio codec with 90x compression factor. Supports 44.1kHz, 24kHz, and 16kHz mono/stereo audio.
PyTorch implementation (with up-to-date tooling) of the SAM / DAC algorithm
A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps
Code for our paper "Generative Adversarial Network with Soft-Dynamic Time Warping and Parallel Reconstruction for Energy Time Series Anomaly Detection" and its extension.
Generating tabular datasets under differential privacy
Learn Generative AI with PyTorch (Manning Publications, 2024)
Synthetic data generators for tabular and time-series data
Released June 10, 2014