The method we proposed uses a multi-scale discriminator which consist of two discriminators with the same structure but different input sizes. The objective function has two parts: one is the adversarial loss, the other is feature matching loss which stabilizes the training and get better ...
每个Discriminator的block的最前面使用MinibatchStdDev[1] 对每个随机向量进行hypersphere normalization[1],即使各元素平方和为向量维度。即ProGAN中的Pixel-wise Norm,但同时用于初始化z。 对模型的参数进行Kaiming/He Init Conclusion 本文实现了一种简单、稳定、对Lr有鲁棒性的高分辨率图像生成方法,但显存消耗较大(训...
The proposed method includes two modules: the diffusion generator and the u-net discriminator. The goal of the first module is to get different information from different levels, enhancing the generalization ability of the generator to the image and improving the stability of the training. Generated...
The key innovation of our approach lies in the integration of a multi-scale attention U-Net discriminator, which allows for more accurate differentiation between subject and background areas in images. By leveraging this architecture, MSA-ESRGAN surpasses traditional methods and several state-of-the-...
GREET [38]: GREET addresses the tendency of existing unsupervised graph representation learning methods to perform smooth learning along all edges, thereby neglecting the heterogeneity of nodes with different attributes. It constructs a homogeneous/heterogeneous edge discriminator to infer the homogeneity/het...
The attention mechanism in the transformer is competent to preserve both global and local information while acting as a discriminator. The proposed GAN model segments the blood vessels more accurately through the adversarial learning process to produce state-of-the-art results. In the preprocessing ...
《ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators》(2020) GitHub:O网页链接 [fig4]《Demystifying Self-Supervised Learning: An Information-Theoretical Framework》(2020) GitHub:O网页链接《Understanding the Difficulty of Training Transformers》(2020) GitHub:O网页链接 [fig5]...
(PseAAC) approach, called g-gap dipeptide composition, which was combined with a customized markov discriminator to design an efficient algorithm called IDMD for predicting sub-Golgi protein types. The accuracy of IDMD reached 74.7%. Then, a support vector machine-based method was developed by ...
(2018) complete the corrupted image by both global and local GAN, in which the small region around the missing areas are adopted in discriminator to improve the performance of training. Additionally, Nazeri et al. (2019) propose the PatchGAN based inpainting network to focus on the patch ...
The attention mechanism [24] was added to the generator to reduce information loss, and the dense connection modules were used in the discriminator to mitigate gradient vanishing [25] and achieve feature reuse [26]. In addition, many other researchers have contributed significantly to automatic ...