http://speech.ee.ntu.edu.tw/~tlkagk/courses/MLDS_2024/Lecture/GANSeqNew.pdf Witryna2 mar 2024 · This paper proposes a simple yet effective module, namely AdaptiveMix, for GANs, which shrinks the regions of training data in the image representation space of the discriminator, and proposes to construct hard samples and narrow down the feature distance between hard and easy samples. Due to the outstanding capability for data …
Improving Conditional Sequence Generative Adversarial Networks …
WitrynaGenerative adversarial networks (GANs) studies have grown exponentially in the past few years. Their impact has been seen mainly in the computer vision field with realistic image and video manipulation, especially generation, making significant advancements. While these computer vision advances have garnered much attention, GAN … WitrynaTowards Unified Scene Text Spotting based on Sequence Generation Taeho Kil · Seonghyeon Kim · Sukmin Seo · Yoonsik Kim · Daehee Kim Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners ... GLeaD: Improving GANs with A Generator-Leading Task Qingyan Bai · Ceyuan Yang · … ct three digit day number
Autoregressive GAN for Semantic Unconditional Head Motion Generation
WitrynaA binocular vision system is a common perception component of an intelligent vehicle. Benefiting from the biomimetic structure, the system is simple and effective. Which are extremely snesitive on external factors, especially missing vision signals. In this paper, a virtual view-generation algorithm based on generative adversarial networks (GAN) is … WitrynaThe model can generate a whole sequence by iteratively constructing the samples. PI-GAN forms a novel adversarial framework with incorporated prior knowledge. We demonstrate the effectiveness of our model on a variety of tasks. 2 Method Problem 1 (Physics-Informed Sequence Generation) Given Ntraining examples fsigN i=1, … WitrynaIn this work, we address the task of unconditional head motion generation to animate still human faces in a low-dimensional semantic space from a single reference pose. Different from traditional audio-conditioned talking head generation that seldom puts emphasis on realistic head motions, we devise a GAN-based architecture that learns to synthesize … ease my feeling