论文标题
AMF：适应性的加权融合，多次微调用于图像分类
AMF: Adaptable Weighting Fusion with Multiple Fine-tuning for Image Classification
论文作者
论文摘要
微调被广泛应用于图像分类任务中，作为转移学习方法。它重新使用源任务中的知识来学习和获得目标任务的高性能。微调能够减轻培训数据不足和新数据昂贵标签的挑战。但是，标准微调在复杂的数据分布中的性能有限。为了解决此问题，我们提出了适应性的多调整方法，该方法可适应地确定每个数据样本的微调策略。在此框架中，定义了多个微调设置和一个策略网络。适应性多调整中的策略网络可以动态调整为最佳权重，以将不同的样本馈入使用不同的微调策略训练的模型。我们的方法在数据集FGVC-Aircraft上优于标准的微调方法，并在数据集中的纹理上胜过2.79％，在Stanford Cars，Cifar-10和Fashion-Mnist上产生了可比的性能。
Fine-tuning is widely applied in image classification tasks as a transfer learning approach. It re-uses the knowledge from a source task to learn and obtain a high performance in target tasks. Fine-tuning is able to alleviate the challenge of insufficient training data and expensive labelling of new data. However, standard fine-tuning has limited performance in complex data distributions. To address this issue, we propose the Adaptable Multi-tuning method, which adaptively determines each data sample's fine-tuning strategy. In this framework, multiple fine-tuning settings and one policy network are defined. The policy network in Adaptable Multi-tuning can dynamically adjust to an optimal weighting to feed different samples into models that are trained using different fine-tuning strategies. Our method outperforms the standard fine-tuning approach by 1.69%, 2.79% on the datasets FGVC-Aircraft, and Describable Texture, yielding comparable performance on the datasets Stanford Cars, CIFAR-10, and Fashion-MNIST.
