ACL'2023: DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models
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Updated
Feb 17, 2024 - Python
ACL'2023: DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models
Update-to-data resources for conditional content generation, including human motion generation, image or video generation and editing.
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.
Few-Shot Diffusion Models
Code for "Optimal Transport-Guided Conditional Score-Based Diffusion Model (NeurIPS, 8,7,7,6)"
Generation of synthetic 12-lead electrocardiograms conditioned on 71 ECG statements from the PTB-XL dataset.
[NeurIPS 2023] VPP: Efficient Conditional 3D Generation via Voxel-Point Progressive Representation
The code for the NeurIPS 2021 paper "A Unified View of cGANs with and without Classifiers".
Controllable Face Generation via pretrained Conditional Adversarial Latent Autoencoder (ALAE)
TRGAN: A Time-Dependent Generative Adversarial Network for Synthetic Transactional Data Generation
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
[ICLR 2022] Toy Experiments for Denoising Likelihood Score Matching for Conditional Score-based Data Generation
TRGAN: A Time-Dependent Generative Adversarial Network for Synthetic Transactional Data Generation
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.
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.
Conditional Generative Adversarial Network for Molecular Dynamics frame generation
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