Recent works on personalized text-to-image generation usually learn to bind a
special token with specific subjects or styles of a few given images by tuning
its embedding through gradient descent. It is natural to question whether we
can optimize the textual inversions by only accessing the process of model
inference. As only requiring the forward computation to determine the textual
inversion retains the benefits of less GPU memory, simple deployment, and
secure access for scalable models. In this paper, we introduce a
\emph{gradient-free} framework to optimize the continuous textual inversion in
an iterative evolutionary strategy. Specifically, we first initialize an
appropriate token embedding for textual inversion with the consideration of
visual and text vocabulary information. Then, we decompose the optimization of
evolutionary strategy into dimension reduction of searching space and
non-convex gradient-free optimization in subspace, which significantly
accelerates the optimization process with negligible performance loss.
Experiments in several applications demonstrate that the performance of
text-to-image model equipped with our proposed gradient-free method is
comparable to that of gradient-based counterparts with variant GPU/CPU
platforms, flexible employment, as well as computational efficiency.

本文提出了一种基于进化策略的梯度自由框架，用于优化文本反演的过程，试图解决个性化文本生成中因梯度下降算法的内存消耗大、部署复杂等问题。实验结果表明，采用该方法生成的文本图像与采用梯度下降方法生成的相比，性能几乎没有损失，但能够获得更高的计算效率和更广泛的适用性。

无梯度文本反演

Gradient-Free Textual Inversion

When searching for policies, reward-sparse environments often lack sufficient
information about which behaviors to improve upon or avoid. In such
environments, the policy search process is bound to blindly search for
reward-yielding transitions and no early reward can bias this search in one
direction or another. A way to overcome this is to use intrinsic motivation in
order to explore new transitions until a reward is found. In this work, we use
a recently proposed definition of intrinsic motivation, Curiosity, in an
evolutionary policy search method. We propose Curiosity-ES, an evolutionary
strategy adapted to use Curiosity as a fitness metric. We compare Curiosity
with Novelty, a commonly used diversity metric, and find that Curiosity can
generate higher diversity over full episodes without the need for an explicit
diversity criterion and lead to multiple policies which find reward.

本文介绍一种利用内在动机机制的进化策略搜索方法 Curiosity-ES，以提高基于奖励的过渡的多样性和产生多种满足奖励的策略。

好奇心创造政策搜索中的多样性

Curiosity creates Diversity in Policy Search

We study the task of replicating the functionality of black-box neural
models, for which we only know the output class probabilities provided for a
set of input images. We assume back-propagation through the black-box model is
not possible and its training images are not available, e.g. the model could be
exposed only through an API. In this context, we present a teacher-student
framework that can distill the black-box (teacher) model into a student model
with minimal accuracy loss. To generate useful data samples for training the
student, our framework (i) learns to generate images on a proxy data set (with
images and classes different from those used to train the black-box) and (ii)
applies an evolutionary strategy to make sure that each generated data sample
exhibits a high response for a specific class when given as input to the black
box. Our framework is compared with several baseline and state-of-the-art
methods on three benchmark data sets. The empirical evidence indicates that our
model is superior to the considered baselines. Although our method does not
back-propagate through the black-box network, it generally surpasses
state-of-the-art methods that regard the teacher as a glass-box model. Our code
is available at: this https URL

本文研究如何通过使用 teacher-student 的框架，以及应用进化策略来生成训练数据样本，以最小化准确率损失，从而复制黑盒神经模型的功能，并且在三个基准数据集上将我们的框架与几种基线和最先进方法进行了比较。

黑盒翻录器：使用生成式进化算法复制黑盒模型

Black-Box Ripper: Copying black-box models using generative evolutionary  algorithms

Reinforcement Learning and the Evolutionary Strategy are two major approaches
in addressing complicated control problems. Both are strong contenders and have
their own devotee communities. Both groups have been very active in developing
new advances in their own domain and devising, in recent years, leading-edge
techniques to address complex continuous control tasks. Here, in the context of
Deep Reinforcement Learning, we formulate a parallelized version of the
Proximal Policy Optimization method and a Deep Deterministic Policy Gradient
method. Moreover, we conduct a thorough comparison between the state-of-the-art
techniques in both camps fro continuous control; evolutionary methods and Deep
Reinforcement Learning methods. The results show there is no consistent winner.

对含复杂控制问题的强化学习和进化策略这两种方法进行了并行化改进，同时在深度强化学习领域比较了进化方法和深度强化学习方法，结果显示两种方法没有一个持续的胜者。