# TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network

arXiv: https://arxiv.org/abs/1703.06412 · HTML: https://mlsanity.com/paper/1703.06412/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2017.

## Built on by (most cited first, 21 of 21)

- [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://mlsanity.com/paper/2111.14822/) (2021)
- [Improved Vector Quantized Diffusion Models](https://mlsanity.com/paper/2205.16007/) (2022)
- [Adversarial Text-to-Image Synthesis: A Review](https://mlsanity.com/paper/2101.09983/) (2021)
- [Multimodal Image Synthesis and Editing: The Generative AI Era](https://mlsanity.com/paper/2112.13592/) (2021)
- [RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model](https://mlsanity.com/paper/2309.00810/) (2023)
- [Multi-modal Machine Learning in Engineering Design: A Review and Future Directions](https://mlsanity.com/paper/2302.10909/) (2023)
- [AI-Generated Images as Data Source: The Dawn of Synthetic Era](https://mlsanity.com/paper/2310.01830/) (2023)
- [Text-to-Image Synthesis: A Decade Survey](https://mlsanity.com/paper/2411.16164/) (2024)
- [Interactive Image Manipulation with Complex Text Instructions](https://mlsanity.com/paper/2211.15352/) (2022)
- [DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training](https://mlsanity.com/paper/2101.07524/) (2021)
- [Level generation and style enhancement -- deep learning for game development overview](https://mlsanity.com/paper/2107.07397/) (2021)
- [on the effectiveness of generative adversarial network on anomaly detection](https://mlsanity.com/paper/2112.15541/) (2021)
- [Synthesizing Forestry Images Conditioned on Plant Phenotype Using a Generative Adversarial Network](https://mlsanity.com/paper/2307.03789/) (2023)
- [Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient](https://mlsanity.com/paper/2401.11261/) (2024)
- [End-to-end Training for Text-to-Image Synthesis using Dual-Text Embeddings](https://mlsanity.com/paper/2502.01507/) (2025)
- [Weakly-supervised Generative Adversarial Networks for medical image classification](https://mlsanity.com/paper/2111.14605/) (2021)
- [Looks Like Magic: Transfer Learning in GANs to Generate New Card Illustrations](https://mlsanity.com/paper/2205.14442/) (2022)
- [Explicit Use of Fourier Spectrum in Generative Adversarial Networks](https://mlsanity.com/paper/2208.01265/) (2022)
- [Fine-grained Text to Image Synthesis](https://mlsanity.com/paper/2412.07196/) (2024)
- [MAGIC-Flow: Multiscale Adaptive Conditional Flows for Generation and Interpretable Classification](https://mlsanity.com/paper/2510.22070/) (2025)
- [CoFi-UCGen: Coarse-to-Fine Unsupervised Conditional Generation without Label Priors](https://mlsanity.com/paper/2606.05652/) (2026)

## Abstract (arXiv)

In this work, we present the Text Conditioned Auxiliary Classifier Generative Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network (GAN) for synthesizing images from their text descriptions. Former approaches have tried to condition the generative process on the textual data; but allying it to the usage of class information, known to diversify the generated samples and improve their structural coherence, has not been explored. We trained the presented TAC-GAN model on the Oxford-102 dataset of flowers, and evaluated the discriminability of the generated images with Inception-Score, as well as their diversity using the Multi-Scale Structural Similarity Index (MS-SSIM). Our approach outperforms the state-of-the-art models, i.e., its inception score is 3.45, corresponding to a relative increase of 7.8% compared to the recently introduced StackGan. A comparison of the mean MS-SSIM scores of the training and generated samples per class shows that our approach is able to generate highly diverse images with an average MS-SSIM of 0.14 over all generated classes.