Deep learning algorithms have a large number of trainable parameters often
with sizes of hundreds of thousands or more. Training this algorithm requires a
large amount of training data and generating a sufficiently large dataset for
these algorithms is costly\cite{noguchi2019image}.
GANs are generative neural networks that use two deep learning networks that
are competing with each other. The networks are generator and discriminator
networks. The generator tries to generate realistic images which resemble the
actual training dataset by approximating the training data distribution and the
discriminator is trained to classify images as real or
fake(generated)\cite{goodfellow2016nips}. Training these GAN algorithms also
requires a large amount of training dataset\cite{noguchi2019image}.
In this study, the aim is to address the question, "Given an unconditioned
pretrained generator network and a pretrained classifier, is it feasible to
develop a conditioned generator without relying on any training dataset?"
The paper begins with a general introduction to the problem. The subsequent
sections are structured as follows: Section 2 provides background information
on the problem. Section 3 reviews relevant literature on the topic. Section 4
outlines the methodology employed in this study. Section 5 presents the
experimental results. Section 6 discusses the findings and proposes potential
future research directions. Finally, Section 7 offers concluding remarks.
The implementation can be accessed
\href{this https URL}{here}.

给定一个无限制的预训练生成网络和一个预训练分类器，通过不依赖于任何训练数据集，开发一个条件生成器是否可行？

无需训练数据集的条件生成对抗网络

Conditioning GAN Without Training Dataset

Recent advances in text-to-image diffusion models have enabled the generation
of diverse and high-quality images. However, generated images often fall short
of depicting subtle details and are susceptible to errors due to ambiguity in
the input text. One way of alleviating these issues is to train diffusion
models on class-labeled datasets. This comes with a downside, doing so limits
their expressive power: (i) supervised datasets are generally small compared to
large-scale scraped text-image datasets on which text-to-image models are
trained, and so the quality and diversity of generated images are severely
affected, or (ii) the input is a hard-coded label, as opposed to free-form
text, which limits the control over the generated images.
In this work, we propose a non-invasive fine-tuning technique that
capitalizes on the expressive potential of free-form text while achieving high
accuracy through discriminative signals from a pretrained classifier, which
guides the generation. This is done by iteratively modifying the embedding of a
single input token of a text-to-image diffusion model, using the classifier, by
steering generated images toward a given target class. Our method is fast
compared to prior fine-tuning methods and does not require a collection of
in-class images or retraining of a noise-tolerant classifier. We evaluate our
method extensively, showing that the generated images are: (i) more accurate
and of higher quality than standard diffusion models, (ii) can be used to
augment training data in a low-resource setting, and (iii) reveal information
about the data used to train the guiding classifier. The code is available at
https://github.com/idansc/discriminative_class_tokens

本研究提出了一种优化自由文本表达，同时通过一个预先训练的分类器提供的判别信号，将生成的图像引导到目标分类的非侵入式微调技术。

用于文本到图像扩散模型的判别类令牌

Discriminative Class Tokens for Text-to-Image Diffusion Models

We present a method for provably defending any pretrained image classifier
against $\ell_p$ adversarial attacks. This method, for instance, allows public
vision API providers and users to seamlessly convert pretrained non-robust
classification services into provably robust ones. By prepending a
custom-trained denoiser to any off-the-shelf image classifier and using
randomized smoothing, we effectively create a new classifier that is guaranteed
to be $\ell_p$-robust to adversarial examples, without modifying the pretrained
classifier. Our approach applies to both the white-box and the black-box
settings of the pretrained classifier. We refer to this defense as denoised
smoothing, and we demonstrate its effectiveness through extensive
experimentation on ImageNet and CIFAR-10. Finally, we use our approach to
provably defend the Azure, Google, AWS, and ClarifAI image classification APIs.
Our code replicating all the experiments in the paper can be found at:
this https URL

本文提出了一种名为 'denoised smoothing' 的方法，通过添加定制训练的去噪器和使用随机平滑，可以将现有的图像分类器转化为 Robust 分类器以防御 'adversarial attacks'，针对 ImageNet 和 CIFAR-10 数据集进行了广泛实验，并成功对 Azure，Google，AWS 和 ClarifAI 的图像分类 API 进行了 'robustness' 测试。

去噪平滑：针对预训练分类器的可证明防御

Denoised Smoothing: A Provable Defense for Pretrained Classifiers

Adversarial examples have raised questions regarding the robustness and
security of deep neural networks. In this work we formalize the problem of
adversarial images given a pretrained classifier, showing that even in the
linear case the resulting optimization problem is nonconvex. We generate
adversarial images using shallow and deep classifiers on the MNIST and ImageNet
datasets. We probe the pixel space of adversarial images using noise of varying
intensity and distribution. We bring novel visualizations that showcase the
phenomenon and its high variability. We show that adversarial images appear in
large regions in the pixel space, but that, for the same task, a shallow
classifier seems more robust to adversarial images than a deep convolutional
network.

本研究在 MNIST 和 ImageNet 数据集上探究了浅层和深度分类器对抗性图像的像素空间，并证明了在相同任务中与深度卷积神经网络相比，浅层分类器的抗干扰性更强，同时也提供了新的展示现象及其高可变性的可视化。