Adversarial generative models, such as Generative Adversarial Networks
(GANs), are widely applied for generating various types of data, i.e., images,
text, and audio. Accordingly, its promising performance has led to the
GAN-based adversarial attack methods in the white-box and black-box attack
scenarios. The importance of transferable black-box attacks lies in their
ability to be effective across different models and settings, more closely
aligning with real-world applications. However, it remains challenging to
retain the performance in terms of transferable adversarial examples for such
methods. Meanwhile, we observe that some enhanced gradient-based transferable
adversarial attack algorithms require prolonged time for adversarial sample
generation. Thus, in this work, we propose a novel algorithm named GE-AdvGAN to
enhance the transferability of adversarial samples whilst improving the
algorithm's efficiency. The main approach is via optimising the training
process of the generator parameters. With the functional and characteristic
similarity analysis, we introduce a novel gradient editing (GE) mechanism and
verify its feasibility in generating transferable samples on various models.
Moreover, by exploring the frequency domain information to determine the
gradient editing direction, GE-AdvGAN can generate highly transferable
adversarial samples while minimizing the execution time in comparison to the
state-of-the-art transferable adversarial attack algorithms. The performance of
GE-AdvGAN is comprehensively evaluated by large-scale experiments on different
datasets, which results demonstrate the superiority of our algorithm. The code
for our algorithm is available at: this https URL

通过优化生成器参数的训练过程并引入梯度编辑机制，GE-AdvGAN 算法能够生成高度可迁移的对抗样本并在执行时间上与现有方法相比保持最小化。

GE-AdvGAN：基于梯度编辑的对抗生成模型改进对抗样本的可传递性

GE-AdvGAN: Improving the transferability of adversarial samples by  gradient editing-based adversarial generative model

While progress in 2D generative models of human appearance has been rapid,
many applications require 3D avatars that can be animated and rendered.
Unfortunately, most existing methods for learning generative models of 3D
humans with diverse shape and appearance require 3D training data, which is
limited and expensive to acquire. The key to progress is hence to learn
generative models of 3D avatars from abundant unstructured 2D image
collections. However, learning realistic and complete 3D appearance and
geometry in this under-constrained setting remains challenging, especially in
the presence of loose clothing such as dresses. In this paper, we propose a new
adversarial generative model of realistic 3D people from 2D images. Our method
captures shape and deformation of the body and loose clothing by adopting a
holistic 3D generator and integrating an efficient and flexible articulation
module. To improve realism, we train our model using multiple discriminators
while also integrating geometric cues in the form of predicted 2D normal maps.
We experimentally find that our method outperforms previous 3D- and
articulation-aware methods in terms of geometry and appearance. We validate the
effectiveness of our model and the importance of each component via systematic
ablation studies.

本文提出了一种从 2D 图像生成逼真的 3D 人物的新方法，并使用多个辨别器和 2D 法线图形式的几何线索来训练模型，取得了在几何和外观方面超过以前 3D 和关节感知方法的性能，通过系统的消融研究验证了模型的有效性和每个组件的重要性。

AG3D: 从 2D 图像集合中学习生成 3D 头像

AG3D: Learning to Generate 3D Avatars from 2D Image Collections

Text generation is a crucial task in NLP. Recently, several adversarial
generative models have been proposed to improve the exposure bias problem in
text generation. Though these models gain great success, they still suffer from
the problems of reward sparsity and mode collapse. In order to address these
two problems, in this paper, we employ inverse reinforcement learning (IRL) for
text generation. Specifically, the IRL framework learns a reward function on
training data, and then an optimal policy to maximum the expected total reward.
Similar to the adversarial models, the reward and policy function in IRL are
optimized alternately. Our method has two advantages: (1) the reward function
can produce more dense reward signals. (2) the generation policy, trained by
"entropy regularized" policy gradient, encourages to generate more diversified
texts. Experiment results demonstrate that our proposed method can generate
higher quality texts than the previous methods.

本论文针对文本生成中奖励稀疏和模式崩溃等问题，提出采用反强化学习方法实现文本生成，即通过学习在训练数据上的奖励函数和最大化预期总奖励的最优策略函数，将奖励和策略函数进行优化，实验结果表明该方法较之前的方法可以生成更高质量的文本。