advantage of recently popular pretrained vision models (PVMs) with extensive datasets, and then incorporate its semantic features into the discriminator through a well-designed spatial cross-attention module. In this way, our proposed semantic-aware discriminator empowered the SR networ...
Semantic-Aware Discriminator for Image Super-Resolution This repository is the official PyTorch implementation of SeD: Semantic-Aware Discriminator for Image Super-Resolution (CVPR24) 🔖 News!!! 2024-3-24: Updated training codes! 2024-4-11: Updated test codes and U+SeD. Generative Adversarial Ne...
增加了Zs和Zt的输入 输出使用Discriminator计算损失 而这个网络结构不是凭空提出来的,是借鉴了这些相关工作: 2.1GRAF(Generative Radiance Fields for 3D-Aware Image Synthesis) 重点: 1.一个标准的conditional GAN, 然后NeRF的部分就放在了生成器里面。 2.生成器的输入是相机的参数(位置,方向,焦点,远近等等),这些...
We re-design the discriminator as a semantic segmentation network, directly using the given semantic label maps as the ground truth for training. By providing stronger supervision to the discriminator as well as to the generator through spatially- and semantically-aware discriminator feedback, we are...
Specifically, the ability of the discriminator is improved by using segmen- tation map to determine the fake areas, which can improve the image quality further. The main contributions of our work are as follows: • We propose a semantic-aware knowledge-guided fr...
We train separate and small local discriminators for each region to distinguish whether the restore patches are real, pushing the patches close to the real facial component shapes. The above loss functions only focus on the face parsing result, which constrains the result to be consistent in the...
2. Again, we can observe that the single scale FDA (FDA) with ResNet101 outperforms most methods that employ adversarial training by instantiating an image transformer and a discriminator [19, 43, 17, 29]. With entropy minimization activated, the single scale FDA (FDA-ENT) ...
DCNN: Dual Cross-current Neural Networks Realized Using An Interactive Deep Learning Discriminator for Fine-grained Objects The main novel design features for constructing a weakly supervised learning backbone model DCNN include (a) extracting heterogeneous data, (b) keeping the ... D Fu,M Rong,EH...
The Minimax Active Learning (MAL) framework also used a discriminator to classify the most diverse samples as compared to the labeled set and paired it with class prototypes to identify the highest en- tropy samples [21]. The Difficulty-awarE Active Learn- ing (DEAL) ar...
Thus, we propose to use a dual-branch discriminator D(x, y) that has two convolution branches for x and y, respectively. The outputs are then summed up for fully connected layers. Such a design allows us to separately regularize the gradient n...