This technical report describes the training of nomic-embed-vision, a highly
performant, open-code, open-weights image embedding model that shares the same
latent space as nomic-embed-text. Together, nomic-embed-vision and
nomic-embed-text form the first unified latent space to achieve high
performance across vision, language, and multimodal tasks.

描述了训练 nomic-embed-vision 的技术报告，它是一种高性能、开放代码、开放权重的图像嵌入模型，与 nomic-embed-text 共享相同的潜在空间。nomic-embed-vision 和 nomic-embed-text 共同构成了首个实现在视觉、语言和多模态任务中获得高性能的统一潜在空间。

诺米嵌入视觉：扩展潜在空间

Nomic Embed Vision: Expanding the Latent Space

In forensic investigations of machine learning models, techniques that
determine a model's data domain play an essential role, with prior work relying
on large-scale corpora like ImageNet to approximate the target model's domain.
Although such methods are effective in finding broad domains, they often
struggle in identifying finer-grained classes within those domains. In this
paper, we introduce an enhanced approach to determine not just the general data
domain (e.g., human face) but also its specific attributes (e.g., wearing
glasses). Our approach uses an image embedding model as the encoder and a
generative model as the decoder. Beginning with a coarse-grained description,
the decoder generates a set of images, which are then presented to the unknown
target model. Successful classifications by the model guide the encoder to
refine the description, which in turn, are used to produce a more specific set
of images in the subsequent iteration. This iterative refinement narrows down
the exact class of interest. A key strength of our approach lies in leveraging
the expansive dataset, LAION-5B, on which the generative model Stable Diffusion
is trained. This enlarges our search space beyond traditional corpora, such as
ImageNet. Empirical results showcase our method's performance in identifying
specific attributes of a model's input domain, paving the way for more detailed
forensic analyses of deep learning models.

这篇论文介绍了一种增强的方法来确定机器学习模型的数据领域和特定属性，并使用图像嵌入模型作为编码器和生成模型作为解码器。该方法通过迭代改进描述并生成更具体的图像，以识别模型输入领域的特定属性，为深度学习模型的详细法医分析铺平了道路。

域桥：基于生成模型的黑盒模型域取证

Domain Bridge: Generative model-based domain forensic for black-box  models

We address the problem of distance metric learning in visual similarity
search, defined as learning an image embedding model which projects images into
Euclidean space where semantically and visually similar images are closer and
dissimilar images are further from one another. We present a weakly supervised
adaptive triplet loss (ATL) capable of capturing fine-grained semantic
similarity that encourages the learned image embedding models to generalize
well on cross-domain data. The method uses weakly labeled product description
data to implicitly determine fine grained semantic classes, avoiding the need
to annotate large amounts of training data. We evaluate on the Amazon fashion
retrieval benchmark and DeepFashion in-shop retrieval data. The method boosts
the performance of triplet loss baseline by 10.6% on cross-domain data and
out-performs the state-of-art model on all evaluation metrics.

该研究提出一种弱监督自适应三元组损失方法来解决视觉相似性搜索中的距离度量学习问题，通过使用弱标记的产品描述数据来隐式地确定细粒度语义类别，从而提高了学习图像嵌入模型的泛化能力，并在 Amazon 时尚检索基准和 DeepFashion 店内检索数据集上实现了优于当前最先进模型的性能。