Improved Training with Curriculum GANs
Not on the map. Why?
Abstract (arXiv)
In this paper we introduce Curriculum GANs, a curriculum learning strategy for training Generative Adversarial Networks that increases the strength of the discriminator over the course of training, thereby making the learning task progressively more difficult for the generator. We demonstrate that this strategy is key to obtaining state-of-the-art results in image generation. We also show evidence that this strategy may be broadly applicable to improving GAN training in other data modalities.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jul 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 1.
4 papers.
- 2022Learning Category-Level Generalizable Object Manipulation Policy via Generative Adversarial Self-Imitation Learning from Demonstrationscited by 15
- 2021Exploring Dropout Discriminator for Domain Adaptationcited by 1
- 2024ObjBlur: A Curriculum Learning Approach With Progressive Object-Level Blurring for Improved Layout-to-Image Generationcited by 1
- 2025CLPSTNet: A Progressive Multi-Scale Convolutional Steganography Model Integrating Curriculum Learningcited by 1
- 2025CLPSTNet: A Progressive Multi-Scale Convolutional Steganography Model Integrating Curriculum Learning23 Apr 2025
- 2024ObjBlur: A Curriculum Learning Approach With Progressive Object-Level Blurring for Improved Layout-to-Image Generation11 Apr 2024
- 2022Learning Category-Level Generalizable Object Manipulation Policy via Generative Adversarial Self-Imitation Learning from Demonstrations4 Mar 2022
- 2021Exploring Dropout Discriminator for Domain Adaptation9 Jul 2021