Methods for watermarking large language models have been proposed that
distinguish AI-generated text from human-generated text by slightly altering
the model output distribution, but they also distort the quality of the text,
exposing the watermark to adversarial detection. More recently, distortion-free
watermarking methods were proposed that require a secret key to detect the
watermark. The prior methods generally embed zero-bit watermarks that do not
provide additional information beyond tagging a text as being AI-generated. We
extend an existing zero-bit distortion-free watermarking method by embedding
multiple bits of meta-information as part of the watermark. We also develop a
computationally efficient decoder that extracts the embedded information from
the watermark with low bit error rate.

扩展了一种零比特的无失真水印方法，将多位元的元信息作为水印的一部分嵌入，并开发了一个计算效率高且具有低比特误码率的解码器来提取水印中的嵌入信息。

大型语言模型的多比特无失真水印技术

Multi-Bit Distortion-Free Watermarking for Large Language Models

Fine-Grained Visual Classification(FGVC) is the task that requires
recognizing the objects belonging to multiple subordinate categories of a
super-category. Recent state-of-the-art methods usually design sophisticated
learning pipelines to tackle this task. However, visual information alone is
often not sufficient to accurately differentiate between fine-grained visual
categories. Nowadays, the meta-information (e.g., spatio-temporal prior,
attribute, and text description) usually appears along with the images. This
inspires us to ask the question: Is it possible to use a unified and simple
framework to utilize various meta-information to assist in fine-grained
identification? To answer this problem, we explore a unified and strong
meta-framework(MetaFormer) for fine-grained visual classification. In practice,
MetaFormer provides a simple yet effective approach to address the joint
learning of vision and various meta-information. Moreover, MetaFormer also
provides a strong baseline for FGVC without bells and whistles. Extensive
experiments demonstrate that MetaFormer can effectively use various
meta-information to improve the performance of fine-grained recognition. In a
fair comparison, MetaFormer can outperform the current SotA approaches with
only vision information on the iNaturalist2017 and iNaturalist2018 datasets.
Adding meta-information, MetaFormer can exceed the current SotA approaches by
5.9% and 5.3%, respectively. Moreover, MetaFormer can achieve 92.3% and 92.7%
on CUB-200-2011 and NABirds, which significantly outperforms the SotA
approaches. The source code and pre-trained models are released
atthis https URL

本文介绍了 MetaFormer 方法，它使用统一和简单的框架利用各种元信息来协助细粒度图像识别。实验证明，MetaFormer 可以有效地使用各种元信息来提高细粒度识别的性能，并且在 iNaturalist2017 和 iNaturalist2018 数据集上可以优于目前的最先进方法。

MetaFormer: 一种用于细粒度识别的统一元框架

MetaFormer: A Unified Meta Framework for Fine-Grained Recognition

Effective training of deep neural networks can be challenging, and there
remain many open questions on how to best learn these models. Recently
developed methods to improve neural network training examine teaching:
providing learned information during the training process to improve downstream
model performance. In this paper, we take steps towards extending the scope of
teaching. We propose a flexible teaching framework using commentaries, learned
meta-information helpful for training on a particular task. We present
gradient-based methods to learn commentaries, leveraging recent work on
implicit differentiation for scalability. We explore diverse applications of
commentaries, from weighting training examples, to parameterising
label-dependent data augmentation policies, to representing attention masks
that highlight salient image regions. We find that commentaries can improve
training speed and/or performance, and provide insights about the dataset and
training process. We also observe that commentaries generalise: they can be
reused when training new models to obtain performance benefits, suggesting a
use-case where commentaries are stored with a dataset and leveraged in future
for improved model training.

提出了一种基于评论的灵活教学框架，使用基于梯度的方法来学习有助于模型训练的元信息，并从权重训练样本、参数化标签相关数据增强策略到表示突出显示显著图像区域的注意力掩码等多个方面探讨了评论的广泛应用。结果表明，评论可以提高训练速度和 / 或性能，并提供关于数据集和训练过程的见解。