Generative adversarial networks (GANs) achieved a remarkable success in high quality image generation in computer vision,and recently, GANs have gained lots of interest from the NLP community as well. However, achieving similar success in NLP would be more challenging due to the discrete nature of...
再用另一个循环神经网络RNN_2对其进行逐词解码,并以上一个解码神经元的输出作为下一个解码神经元的输入,生成Dialogue下文,需要注意的是:在解码前需配置“开始”标记 _,用于指示解码器Decoder开启Dialogue下文首词(or 字)的生成,并配置“结束”标记 _,用于指示解码器结束当前的 Text Generation 进程...
简而言之就是每得到一个新的生成词，就结合此前生成的前序文本估计最终reward，并作为该生成词单独的reward，SeqGAN的论文中使用蒙特卡洛搜索[21]（Monte Carlo Search，MC search）的方法计算部分生成序列对于整体reward的估计值。
Gal, R., et al.: An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprintarXiv:2208.01618(2022) Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM63(11), 139–144 (2020) ...
We aim to generate realistic images from text descriptions using GAN architecture. The network that we have designed is used for image generation for two datasets: MSCOCO and CUBS. - ayansengupta17/GAN
Diffusion-LMImproves Controllable Text Generation continuousdiffusion model 基于classifier 的思想,从Diffusion models beat GANs on image synthesis启发而来。 本文重在复杂,细粒度的控制生成。本文的生成是非自回归式的。 Motivation:作者希望实现可控生成。但是为每个控制任务更新LM(language model)参数可能很昂贵,而且...
Use advanced data augmentation techniques, such as synthetic handwritten text generation using GANs, to create balanced datasets. To address the challenges of computational resources, utilize distributed training on GPUs or cloud platforms, apply mixed-precision, batch normalization, and dimensionality reduct...
文中将GANs模型比作一种特殊形式的Actor-Critic,并比较了两者各自的特点以及后续的改进技术在两者上的适配情况。试想一下,既然强化学习技术帮助GAN解决了在离散型数据上的梯度传播问题,那么同为强化学习的Actor-Critic也为对抗式文本生成提供了另外一种可能。 5.6. IRGAN:两个检索模型的对抗 IRGAN[25]这篇工作发表于...
(2023). An image is worth one word: Personalizing text-to-image generation using textual inversion. In ICLR. Gal, R., Arar, M., Atzmon, Y., Bermano, A.H., Chechik, G., & Cohen-Or, D. (2023). Designing an encoder for fast personalization of text-to-image models. In Siggraph....
Experient: synthetic data environment, poem generation, speech language generation and music generation Conclusion: this is the first work extending GANs to generate sequences of discrete tokens Comment: 出发点有两个:1. 怎么解决GAN+discrete data的问题 2. 怎么解决评价partially generated sentence的问题。