Generating Video from Images using GANs
Anoosh G P1, Chetan G2, Mohan Kumar M3, Priyanka BN4, Nagashree Nagara5
1Anoosh G P, Department of Computer Science and Engineering Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
2Chetan G, Department of Computer Science and Engineering Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
3Mohan Kumar M, Department of Computer Science and Engineering Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
4Priyanka BN, Department of Computer Science and Engineering Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
5Nagashree Nagaraj, Department of Computer Science and Engineering Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
Manuscript received on July 22, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on August 10, 2020. | PP: 377-380 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.J75600891020 | DOI: 10.35940/ijitee.J7560.0891020
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Abstract: Generative adversarial networks are a category of neural networks used extensively for the generation of a wide range of content. The generative models are trained through an adversarial process that offers a lot of potential in the world of deep learning. GANs are a popular approach to generate new data from random noise vector that are similar or have the same distribution as that in the training data set. The Generative Adversarial Networks (GANs) approach has been proposed to generate more realistic images. An extension of GANs is the conditional GANs which allows the model to condition external information. Conditional GANs have seen increasing uses and more implications than ever. We also propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models, a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. Our work aims at highlighting the uses of conditional GANs specifically with Generating images. We present some of the use cases of conditional GANs with images specifically in video generation.
Keywords: Generative adversarial networks (GANs), Generative Model, Discriminative Model, Video Generation.
Scope of the Article: Generative adversarial networks (GANs)