Conditional Generative Adversarial Network for Molecular Dynamics frame generation
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Updated
Jan 16, 2023 - Python
Conditional Generative Adversarial Network for Molecular Dynamics frame generation
The aim of this work is to generate new face images similar to training ones (the CelebA dataset) according to user specified attributes. To do that we ended up with an implementation of a Versatile Auxiliary Classifier + GAN.
MSc Thesis on Conditional dMRI Generative AI Models and their applicability in the decreasing scan acquisition times and bettering of patient's quality of life.
A partial pytorch implementation of "Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models" for practice
Chinese couplet generation with transformer and simple transformer-based variants.
[ICLR 2022] Toy Experiments for Denoising Likelihood Score Matching for Conditional Score-based Data Generation
Official PyTorch implementation of "Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis" (ICML 2024).
TRGAN: A Time-Dependent Generative Adversarial Network for Synthetic Transactional Data Generation
Forward-backward conditional sampling
Code for the paper "FAME: Fragment-based Conditional Molecular Generation for Phenotypic Drug Discovery", published on SDM 2022.
[ICLR 2022] Denoising Likelihood Score Matching for Conditional Score-based Data Generation
TRGAN: A Time-Dependent Generative Adversarial Network for Synthetic Transactional Data Generation
Controllable Face Generation via pretrained Conditional Adversarial Latent Autoencoder (ALAE)
The code for the NeurIPS 2021 paper "A Unified View of cGANs with and without Classifiers".
[NeurIPS 2023] VPP: Efficient Conditional 3D Generation via Voxel-Point Progressive Representation
Generation of synthetic 12-lead electrocardiograms conditioned on 71 ECG statements from the PTB-XL dataset.
Code for "Optimal Transport-Guided Conditional Score-Based Diffusion Model (NeurIPS, 8,7,7,6)"
Few-Shot Diffusion Models
This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.
Update-to-data resources for conditional content generation, including human motion generation, image or video generation and editing.
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