Generative artificial intelligence (GenAI) has made significant progress in
understanding world knowledge and generating content from human languages
across various modalities, like text-to-text large language models,
text-to-image stable diffusion, and text-to-video Sora. While in this paper, we
investigate the capability of GenAI for text-to-model generation, to see
whether GenAI can comprehend hyper-level knowledge embedded within AI itself
parameters. Specifically, we study a practical scenario termed
train-once-for-all personalization, aiming to generate personalized models for
diverse end-users and tasks using text prompts. Inspired by the recent
emergence of neural network diffusion, we present Tina, a text-conditioned
neural network diffusion for train-once-for-all personalization. Tina leverages
a diffusion transformer model conditioned on task descriptions embedded using a
CLIP model. Despite the astronomical number of potential personalized tasks
(e.g., $1.73\times10^{13}$), by our design, Tina demonstrates remarkable
in-distribution and out-of-distribution generalization even trained on small
datasets ($\sim 1000$). We further verify whether and how \Tina understands
world knowledge by analyzing its capabilities under zero-shot/few-shot image
prompts, different numbers of personalized classes, prompts of natural language
descriptions, and predicting unseen entities.

通过研究 train-once-for-all 个性化场景，本文调查了生成人工智能（GenAI）的文本到模型生成能力，以探讨其是否能理解内嵌在 AI 参数中的超级级别知识。作者提出了 Tina，一个基于文本的神经网络扩散模型，以实现 train-once-for-all 个性化。研究结果显示，即使在小数据集上进行训练（约 1000 个），Tina 表现出显着的分布内和分布外泛化能力，进一步验证了其对世界知识的理解能力。

文本模型：基于文本条件的神经网络扩散用于一次性训练的个性化

Text-to-Model: Text-Conditioned Neural Network Diffusion for  Train-Once-for-All Personalization

Bagging has achieved great success in the field of machine learning by
integrating multiple base classifiers to build a single strong classifier to
reduce model variance. The performance improvement of bagging mainly relies on
the number and diversity of base classifiers. However, traditional deep
learning model training methods are expensive to train individually and
difficult to train multiple models with low similarity in a restricted dataset.
Recently, diffusion models, which have been tremendously successful in the
fields of imaging and vision, have been found to be effective in generating
neural network model weights and biases with diversity. We creatively propose a
Bagging deep learning training algorithm based on Efficient Neural network
Diffusion (BEND). The originality of BEND comes from the first use of a neural
network diffusion model to efficiently build base classifiers for bagging. Our
approach is simple but effective, first using multiple trained model weights
and biases as inputs to train autoencoder and latent diffusion model to realize
a diffusion model from noise to valid neural network parameters. Subsequently,
we generate several base classifiers using the trained diffusion model.
Finally, we integrate these ba se classifiers for various inference tasks using
the Bagging method. Resulting experiments on multiple models and datasets show
that our proposed BEND algorithm can consistently outperform the mean and
median accuracies of both the original trained model and the diffused model. At
the same time, new models diffused using the diffusion model have higher
diversity and lower cost than multiple models trained using traditional
methods. The BEND approach successfully introduces diffusion models into the
new deep learning training domain and provides a new paradigm for future deep
learning training and inference.

提出了一种基于高效神经网络扩散（BEND）的 Bagging 深度学习训练算法，通过使用多个训练的扩散模型构建基础分类器，实现多样性和降低成本，取得了比传统方法更好的准确性和多样性，并成功引入了扩散模型进入新的深度学习训练领域。