# InfoMax-GAN: Improved Adversarial Image Generation via Information Maximization and Contrastive Learning

arXiv: https://arxiv.org/abs/2007.04589 · HTML: https://mlsanity.com/paper/2007.04589/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2020.

## Built on by (most cited first, 30 of 37)

- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Cross-Modal Contrastive Learning for Text-to-Image Generation](https://mlsanity.com/paper/2101.04702/) (2021)
- [Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning](https://mlsanity.com/paper/2205.09542/) (2022)
- [Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspective](https://mlsanity.com/paper/2408.06741/) (2024)
- [Deepfake Network Architecture Attribution](https://mlsanity.com/paper/2202.13843/) (2022)
- [Training GANs with Stronger Augmentations via Contrastive Discriminator](https://mlsanity.com/paper/2103.09742/) (2021)
- [Progressive Open Space Expansion for Open-Set Model Attribution](https://mlsanity.com/paper/2303.06877/) (2023)
- [Dual Contrastive Loss and Attention for GANs](https://mlsanity.com/paper/2103.16748/) (2021)
- [Discovering Transferable Forensic Features for CNN-generated Images Detection](https://mlsanity.com/paper/2208.11342/) (2022)
- [Learning to Disentangle GAN Fingerprint for Fake Image Attribution](https://mlsanity.com/paper/2106.08749/) (2021)
- [Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning](https://mlsanity.com/paper/2403.07240/) (2024)
- [Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks](https://mlsanity.com/paper/2102.12033/) (2021)
- [Learning Probabilistic Models from Generator Latent Spaces with Hat EBM](https://mlsanity.com/paper/2210.16486/) (2022)
- [Data-Independent Operator: A Training-Free Artifact Representation Extractor for Generalizable Deepfake Detection](https://mlsanity.com/paper/2403.06803/) (2024)
- [Exploring The Effect of High-frequency Components in GANs Training](https://mlsanity.com/paper/2103.11093/) (2021)
- [Self-Supervised GANs with Label Augmentation](https://mlsanity.com/paper/2106.08601/) (2021)
- [BIGRoC: Boosting Image Generation via a Robust Classifier](https://mlsanity.com/paper/2108.03702/) (2021)
- [Graph Contrastive Learning with Generative Adversarial Network](https://mlsanity.com/paper/2308.00535/) (2023)
- [Augmentation-Aware Self-Supervision for Data-Efficient GAN Training](https://mlsanity.com/paper/2205.15677/) (2022)
- [Toward Scalable Image Feature Compression: A Content-Adaptive and Diffusion-Based Approach](https://mlsanity.com/paper/2410.06149/) (2024)
- [So-Fake: Benchmarking and Explaining Social Media Image Forgery Detection](https://mlsanity.com/paper/2505.18660/) (2025)
- [AI-Generated Image Detection: An Empirical Study and Future Research Directions](https://mlsanity.com/paper/2511.02791/) (2025)
- [NoiseTransfer: Image Noise Generation with Contrastive Embeddings](https://mlsanity.com/paper/2301.13554/) (2023)
- [DADM: Dual Alignment of Domain and Modality for Face Anti-spoofing](https://mlsanity.com/paper/2503.00429/) (2025)
- [Beyond Semantic Features: Pixel-level Mapping for Generalized AI-Generated Image Detection](https://mlsanity.com/paper/2512.17350/) (2025)
- [F-Drop&Match: GANs with a Dead Zone in the High-Frequency Domain](https://mlsanity.com/paper/2106.02343/) (2021)
- [Each Attribute Matters: Contrastive Attention for Sentence-based Image Editing](https://mlsanity.com/paper/2110.11159/) (2021)
- [Boosting Star-GANs for Voice Conversion with Contrastive Discriminator](https://mlsanity.com/paper/2209.10088/) (2022)
- [MACO: A Modality Adversarial and Contrastive Framework for Modality-missing Multi-modal Knowledge Graph Completion](https://mlsanity.com/paper/2308.06696/) (2023)
- [Few-Shot Class-Incremental Model Attribution Using Learnable Representation From CLIP-ViT Features](https://mlsanity.com/paper/2503.08148/) (2025)

## Abstract (arXiv)

While Generative Adversarial Networks (GANs) are fundamental to many generative modelling applications, they suffer from numerous issues. In this work, we propose a principled framework to simultaneously mitigate two fundamental issues in GANs: catastrophic forgetting of the discriminator and mode collapse of the generator. We achieve this by employing for GANs a contrastive learning and mutual information maximization approach, and perform extensive analyses to understand sources of improvements. Our approach significantly stabilizes GAN training and improves GAN performance for image synthesis across five datasets under the same training and evaluation conditions against state-of-the-art works. In particular, compared to the state-of-the-art SSGAN, our approach does not suffer from poorer performance on image domains such as faces, and instead improves performance significantly. Our approach is simple to implement and practical: it involves only one auxiliary objective, has a low computational cost, and performs robustly across a wide range of training settings and datasets without any hyperparameter tuning. For reproducibility, our code is available in Mimicry: https://github.com/kwotsin/mimicry.