How to Train Vision Transformer on Small-scale Datasets?

Hanan Gani, Muzammal Naseer, Mohammad Yaqub
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引用次数: 15

Abstract

Vision Transformer (ViT), a radically different architecture than convolutional neural networks offers multiple advantages including design simplicity, robustness and state-of-the-art performance on many vision tasks. However, in contrast to convolutional neural networks, Vision Transformer lacks inherent inductive biases. Therefore, successful training of such models is mainly attributed to pre-training on large-scale datasets such as ImageNet with 1.2M or JFT with 300M images. This hinders the direct adaption of Vision Transformer for small-scale datasets. In this work, we show that self-supervised inductive biases can be learned directly from small-scale datasets and serve as an effective weight initialization scheme for fine-tuning. This allows to train these models without large-scale pre-training, changes to model architecture or loss functions. We present thorough experiments to successfully train monolithic and non-monolithic Vision Transformers on five small datasets including CIFAR10/100, CINIC10, SVHN, Tiny-ImageNet and two fine-grained datasets: Aircraft and Cars. Our approach consistently improves the performance of Vision Transformers while retaining their properties such as attention to salient regions and higher robustness. Our codes and pre-trained models are available at: https://github.com/hananshafi/vits-for-small-scale-datasets.
如何在小尺度数据集上训练视觉转换器?
视觉变压器(Vision Transformer, ViT)是一种与卷积神经网络完全不同的架构,它在许多视觉任务上具有多种优势,包括设计简单、鲁棒性和最先进的性能。然而,与卷积神经网络相比,Vision Transformer缺乏固有的归纳偏差。因此,这类模型的成功训练主要归功于在大规模数据集上的预训练,如ImageNet的1.2M或JFT的300M图像。这阻碍了Vision Transformer对小规模数据集的直接适应。在这项工作中,我们证明了自监督归纳偏差可以直接从小规模数据集中学习,并作为一种有效的权重初始化方案进行微调。这允许在没有大规模预训练、改变模型架构或损失函数的情况下训练这些模型。我们提出了在五个小数据集(包括CIFAR10/100, CINIC10, SVHN, Tiny-ImageNet)和两个细粒度数据集(飞机和汽车)上成功训练单片和非单片视觉变压器的彻底实验。我们的方法不断提高视觉变压器的性能,同时保留其特性,如对显著区域的关注和更高的鲁棒性。我们的代码和预训练模型可在:https://github.com/hananshafi/vits-for-small-scale-datasets。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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