GFMN在图像生成上取得了最佳性能且避免了对抗学习的不稳定性。类似于GANs,GFMN通过生成数据反向传播更新参数来训练。这种反向传播过程,再结合对抗学习的不稳定性,应用于离散数据极具挑战性。然而,GFMN对于离散数据的有效性还未被研究,特征匹配网络应用于离散数据是否会遇到挑战仍然未知。因此本文针对此问题展开研究。 贡...
【1】High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs https://arxiv.org/pdf/1711.11585v1.pdf">https://arxiv.org/pdf/1711.11585v1.pdf 【2】Perceptual Losses for Real-Time Style Transfer and Super-Resolution https://arxiv.org/pdf/1603.08155.pdf...
Maps generated by many visual Simultaneous Localization and Mapping algorithms consist of geometric primitives such as points, lines or planes. These maps ... A Iqbal,NR Gans - 《Journal of Intelligent & Robotic Systems》 被引量: 0发表: 2020年 加载更多来源...
By employing SS-GANs, it becomes possible to fine-tune pre-trained transformer models like BERT using unlabeled data, thereby improving intent detection performance without the need for extensive labeled datasets. This article introduces a novel approach called Joint-Average Mean and Variance Feature ...
We believe the success and effectiveness of the GANs in medical imaging Yi et al. (2019), and particularly in modelling disease evolution (Bowles et al., 2018, Wegmayr et al., 2019) and image generation Kwon et al. (2019), can positively impact the Stroke domain. We present pioneering ...
: Deep learning with Generative Adversarial Networks [Book] Videos Generative Adversarial Networks by... neural networks with adversarial training [arXiv] CM-GANs: Cross-modal Generative Adversarial智能推荐Graphical Generative Adversarial Networks Graphical Generative Adversarial Networks Chongxuan Li, Max We...
【1】High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs https://arxiv.org/pdf/1711.11585v1.pdf">https://arxiv.org/pdf/1711.11585v1.pdf 【2】Perceptual Losses for Real-Time Style Transfer and Super-Resolution https://arxiv.org/pdf/1603.08155.pdf...
To cope with the high diversity of natural images, they either rely on the unstable GANs that are difficult to train and prone to artifacts, or resort to explicit references from high-resolution (HR) images that are usually unavailable. In this work, we propose Feature Matching SR (FeMaSR)...
Deep learning models include convolutional neural networks (CNNs) [19], deep belief networks (DBNs) [20], recurrent neural networks (RNNs), generative adversarial networks (GANs) [21] and other classic models, as well as their variants and combined models. As a variant of an RNN model, a...
The 2D3D-DescNet is optimized using the mini-max two-player game framework, a foundational concept in generative adversarial networks (GANs), which is composed of two neural networks: the generator and the discriminator. These networks are trained simultaneously in a competitive setting. The generat...