This paper introduces conditional generative adversarial networks (CGANs), which extend the original generative adversarial networks (GANs) by conditioning the generation process on additional information. The generator and discriminator are both conditioned on an extra variable y, which can be class labels, data from other modalities, or other auxiliary information. This allows for more controlled generation of data, such as generating MNIST digits conditioned on class labels. The model is shown to generate realistic samples and can be used for multi-modal learning, such as image tagging, where it can generate descriptive tags not present in the training data. The paper presents two experiments: one on the MNIST dataset, where the model generates digits conditioned on class labels, and another on the MIR Flickr 25,000 dataset for multi-modal learning. In the image tagging experiment, the model is used to generate descriptive tags for images based on their features. The results show that the conditional adversarial net performs well in generating realistic samples and can be used for multi-modal learning tasks. The paper also discusses related work in multi-modal learning and image labeling, highlighting the challenges of scaling supervised neural networks to handle a large number of output categories and the importance of probabilistic one-to-many mappings. It also discusses the use of conditional probabilistic generative models for multi-modal learning and the potential of CGANs in this area. The paper concludes that the results are preliminary but show the potential of CGANs for interesting and useful applications. Future work includes exploring more sophisticated models and a more detailed analysis of their performance and characteristics. The authors also mention that using multiple tags at the same time could lead to better results and that constructing a joint training scheme to learn the language model is an area for future research.This paper introduces conditional generative adversarial networks (CGANs), which extend the original generative adversarial networks (GANs) by conditioning the generation process on additional information. The generator and discriminator are both conditioned on an extra variable y, which can be class labels, data from other modalities, or other auxiliary information. This allows for more controlled generation of data, such as generating MNIST digits conditioned on class labels. The model is shown to generate realistic samples and can be used for multi-modal learning, such as image tagging, where it can generate descriptive tags not present in the training data. The paper presents two experiments: one on the MNIST dataset, where the model generates digits conditioned on class labels, and another on the MIR Flickr 25,000 dataset for multi-modal learning. In the image tagging experiment, the model is used to generate descriptive tags for images based on their features. The results show that the conditional adversarial net performs well in generating realistic samples and can be used for multi-modal learning tasks. The paper also discusses related work in multi-modal learning and image labeling, highlighting the challenges of scaling supervised neural networks to handle a large number of output categories and the importance of probabilistic one-to-many mappings. It also discusses the use of conditional probabilistic generative models for multi-modal learning and the potential of CGANs in this area. The paper concludes that the results are preliminary but show the potential of CGANs for interesting and useful applications. Future work includes exploring more sophisticated models and a more detailed analysis of their performance and characteristics. The authors also mention that using multiple tags at the same time could lead to better results and that constructing a joint training scheme to learn the language model is an area for future research.