Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks. arXiv, (2009):1–20,2015.》2015年,GAN + semi-supervised learning。本文作者强调了 active learning 和 semi-superviesd learning 的不同。 其他: self-taught learning algorithm:《 Rajat Raina, Alexis Battle, ...
The purpose of active learning is to significantly reduce the cost of annotation while ensuring the good performance of the model. In this paper, we propose a novel active learning method based on the combination of pool and synthesis named dual generative adversarial active learning (DGAAL), ...
We propose a new active learning by query synthesis approach using Generative Adversarial Networks (GAN). Different from regular active learning, the resulting algorithm adaptively synthesizes training instances for querying to increase learning speed. We generate queries according to the uncertainty princip...
In this paper, we present a new supervised anomaly detector through introducing the novel Ensemble Active Learning Generative Adversarial Network (EAL-GAN). EAL-GAN is a conditional GAN having a unique one generator versus multiple discriminators architecture where anomaly detection is implemented by ...
A generative adversarial network, or GAN, is a type of deep learning model typically used in unsupervised machine learning but also adaptable for semi-supervised and supervised learning. GANs are used to generate high-quality data similar to the training dataset. As a subset of generative AI, GA...
Generative adversarial networks (GANs) are an exciting recent innovation in machine learning. 生成式对抗网络(GANs)是机器学习领域最近一项令人兴奋的创新。 GANs are generative models: they create new data instances that resemble your training data. ...
Generative adversarial network (GAN)41 dynamics: We employ a discriminator network, resonating with GAN structures, which discerns between genuine and generated cell data. This guides the generator towards producing more authentic single-cell data. The resultant loss function for the generator discussed...
Generative adversarial network(GAN) is an active branch of deep learning field, which has become a popular research direction in the field of artificial intelligence. GAN adopts an unsupervised learning method and automatically learns from the source data, which can produce amazing effects without arti...
Generative Adversarial Nets (GANs) are a class of generative models that have gained significant attention in the field of deep learning. GANs were first introduced by Ian Goodfellow and his colleagues in 2014. This article aims to provide a comprehensive overview of GANs and delve into their var...
Generative AI’s ability to create new content and generate realistic images and videos has already been utilized to streamline workflows in a variety of sectors including gaming, advertising, and design fields. However, there are also concerns about this technology’s ethical implications, particularly...