In this paper, we propose a new conditional generative adversarial network framework, namely. Contrastive Generative Adversarial Networks (ContraGAN). Our ...
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Aug 1, 2023 · We present GACN, a novel Generative Adversarial Contrastive learning Network for graph representation learning.
Our contrastive learning approach contrasts the sample with local feature maps of itself instead of contrasting a given sample with other instances as in ...
May 3, 2024 · The present work introduces a new approach by integrating contrastive learning into the GAN framework for enhanced map synthesis.
This paper proposes an unsupervised image-to-image (UI2I) translation model, called Perceptual Contrastive Generative Adversarial Network (PCGAN), ...
In this paper, we propose ContraGAN that considers relations between multiple image embeddings in the same batch (data-to-data relations) as well as the data-to ...
Sep 6, 2024 · PDF | Conditional image synthesis is the task to generate high-fidelity diverse images using class label information.
VCL-GAN: A Variational Contrastive Learning Generative Adversarial ...
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Generative Adversarial Networks (GANs) have worked well for image generation, but recent works have shown that their generated images lack diversity.
CL-GAN: Contrastive Learning-Based Generative Adversarial Network for ...
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Feb 18, 2023 · This paper proposes a novel deep learning method, contrastive learning-based Generative Adversarial Network (CL-GAN) for modality transfer with limited paired ...
Conditional generative adversarial networks (cGANs) target at synthesizing diverse images given the input condi- tions and latent codes, but unfortunately, ...