TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models
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Updated
Mar 7, 2024 - Python
TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models
Generative Modeling using GANs, DCGANs, WGAN, WGAN-GP, ProGAN, Conditional GANs and Controllable Generation.
[EMNLP2022] BioNLI: Generating a Biomedical NLI Dataset Using Lexico-semantic Constraints for Adversarial Examples
[ACL2023] NeuroStructural Decoding: Neural Text Generation with Structural Constraints
Stay upto date with recent papers in the field!
Code for my Bachelor thesis on Semantic Image Synthesis with Score-Based Generative Models
This repository contains the implementation of the project: "Controlling DDPMs and VAEs through EBMs" for the Deep Generative Models class @ UIC
Code for CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models
Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled, TMLR 2024
Controllable mage captioning model with unsupervised modes
Code for SMERTI for Semantic Text Exchange.
Source code of our TACL paper "Controllable Summarization with Constrained Markov Decision Process"
Code for SAPPHIRE: Approaches for Enhanced Concept-to-Text Generation (https://aclanthology.org/2021.inlg-1.21/) INLG 2021 Best Long Paper.
[Paperlist] Awesome paper list of controllable text generation via latent auto-encoders. Contributions of any kind are welcome.
[KBS] PCAE: A Framework of Plug-in Conditional Auto-Encoder for Controllable Text Generation PyTorch Implementation
Implementation of Collage Diffusion (https://arxiv.org/abs/2303.00262)
This is the official implementation of CCLAP for Controllable Chinese Landscape Painting generation. (ICME2023, oral)
Implementing a Wassertein GAN with gradient penalty in pytorch
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