Attention Mechanism for Contrastive Learning in GAN-based Image-to-Image Translation
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Abstract (arXiv)
Using real road testing to optimize autonomous driving algorithms is time-consuming and capital-intensive. To solve this problem, we propose a GAN-based model that is capable of generating high-quality images across different domains. We further leverage Contrastive Learning to train the model in a self-supervised way using image data acquired in the real world using real sensors and simulated images from 3D games. In this paper, we also apply an Attention Mechanism module to emphasize features that contain more information about the source domain according to their measurement of significance. Finally, the generated images are used as datasets to train neural networks to perform a variety of downstream tasks to verify that the approach can fill in the gaps between the virtual and real worlds.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 18 references that are in the map. First posted on arXiv 23 Feb 2023.
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References · 13
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
- 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,626
- 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
- 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,198
- 2016Least Squares Generative Adversarial Networkscited by 1,001
- 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 860
- 2016Playing for Data: Ground Truth from Computer Gamescited by 818
- 2020Self-supervised Learning: Generative or Contrastivecited by 694
- 2020Contrastive Learning for Unpaired Image-to-Image Translationcited by 639
- 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 565
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- 2016Invertible Conditional GANs for image editingcited by 118
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