In recent years, we have witnessed a considerable increase in performance in
image classification tasks. This performance improvement is mainly due to the
adoption of deep learning techniques. Generally, deep learning techniques
demand a large set of annotated data, making it a challenge when applying it to
small datasets. In this scenario, transfer learning strategies have become a
promising alternative to overcome these issues. This work aims to compare the
performance of different pre-trained neural networks for feature extraction in
image classification tasks. We evaluated 16 different pre-trained models in
four image datasets. Our results demonstrate that the best general performance
along the datasets was achieved by CLIP-ViT-B and ViT-H-14, where the
CLIP-ResNet50 model had similar performance but with less variability.
Therefore, our study provides evidence supporting the choice of models for
feature extraction in image classification tasks.

比较了不同的预训练神经网络在图像分类任务中的特征提取性能，结果表明 CLIP-ViT-B 和 ViT-H-14 模型在多个数据集上取得了最好的表现，CLIP-ResNet50 模型表现类似但变异性较小。因此，我们的研究证明了在图像分类任务中选择模型进行特征提取的合理性。

图像分类中预训练模型特征提取评估

An evaluation of pre-trained models for feature extraction in image  classification

Task-oriented dialog systems are becoming pervasive, and many companies
heavily rely on them to complement human agents for customer service in call
centers. With globalization, the need for providing cross-lingual customer
support becomes more urgent than ever. However, cross-lingual support poses
great challenges---it requires a large amount of additional annotated data from
native speakers. In order to bypass the expensive human annotation and achieve
the first step towards the ultimate goal of building a universal dialog system,
we set out to build a cross-lingual state tracking framework. Specifically, we
assume that there exists a source language with dialog belief tracking
annotations while the target languages have no annotated dialog data of any
form. Then, we pre-train a state tracker for the source language as a teacher,
which is able to exploit easy-to-access parallel data. We then distill and
transfer its own knowledge to the student state tracker in target languages. We
specifically discuss two types of common parallel resources: bilingual corpus
and bilingual dictionary, and design different transfer learning strategies
accordingly. Experimentally, we successfully use English state tracker as the
teacher to transfer its knowledge to both Italian and German trackers and
achieve promising results.

本研究旨在通过预训练源语言状态追踪器为教师并将其知识传递到目标语言的学生状态追踪器以实现跨语言支持。使用双语语料库或双语字典作为基础，设计不同的转移学习策略以实现任务导向的对话系统。在英语状态跟踪器为教师的情况下，将其知识传递到意大利语和德语跟踪器中，取得了很好的实验效果。