We frame GANs within the wider landscape of algorithms for learning in implicit generative models--models that only specify a stochastic procedure with which to generate data--and relate these ideas to modelling problems in related fields, such as econometrics and approximate Bayesian computation. We...
This paper presents a learning by teaching (LBT) approach to learning implicit models, which intrinsically avoids the mode collapse problem by optimizing a KL-divergence rather than the JS-divergence in GANs. In LBT, an auxiliary density estimator is introduced to fit the implicit model's ...
作者将有条件图像生成任务看作一个因果过程:标签决定图像分布(L→I,L:标签的随机向量,I:图像随机变量) 作者提出causal implicit generative models (CiGMs),其允许模型从真实样本和真实干预分布中采样。且若generator基于因果图构造,则该模型可以用对抗训练方法训练。作者将条件采样和干预采样应用到二值特征标签(如youn...
生成特征匹配网络(Generative feature matching network, GFMN)是一种图像隐式生成方法,其通过将预训练神经网络的特征进行矩匹配来实现,详见论文Learning Implicit Generative Models by Matching Perceptual Features。 本文针对序列数据提出SeqGFMN,在三个生成任务上做了实验:无条件文本生成、类别条件文本生成和无监督文本...
We propose an adversarial training procedure for learning a causal implicit generative model for a given causal graph. We show that adversarial training can be used to learn a generative model with true observational and interventional distributions if the generator architecture is consistent with the ...
Perceptual features (PFs) have been used with great success in tasks such as transfer learning, style transfer, and super-resolution. However, the efficacy of PFs as key source of information for learning generative models is not well studied. We investigate here the use of PFs in the context...
We are interested in directed generative models that are capable of producing explicit density estimates of the data distribution, i.e., models that estimate a probability density function (PDF) over a sample space, and we will leave the examination of most implicit density estimators, i.e., ...
2、隐式对齐 implicit 学习如何在模型训练期间潜在地对齐数据。两种方法:图模型、神经网络模型(使用attention机制) 图像字幕中,注意力机制将允许解码器(通常是 RNN)在生成每个连续单词时专注于图像的特定部分; 问答任务,允许将问题中的单词与信息源的子组件(例如一段文本[236]、图像[65]或视频序列)对齐。
Here, the authors demonstrate that reinforcement learning mediates this process in implicit motor learning, maximizing rewards and minimizing punishments. Taisei Sugiyama , Nicolas Schweighofer & Jun Izawa Article 31 March 2023 | Open Access Meta-learning biologically plausible plasticity rules with ...
5.1.4 Additional Information -- Application of Generative model Density Estimation Outlier Detection Prior Construction Dataset Summarization Deep Generative Models Autoregressive Models Explicit Latent Variable Models Implicit Generative Models Energy-based Models Exact-likelihood Models based on normalizing Flows...