| 基于域通道知识鉴别框架的跨域少样本图像分类 |
投稿时间：2024-03-16 修订日期：2024-04-18  点此下载全文 |
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| 中文摘要:现有的跨领域少样本分类模型受限于域特定因素的干扰，限制了其有效性。为了克服这个问题，我们提出了一种基于高斯仿射的通道鉴别网络。具体来讲，所提出的学习框架包含随机高斯仿射模块和域通道鉴别模块，在随机高斯仿射模块中，我们通过对特征的充分统计量进行高斯扰动以生成区别于源域数据分布的全新特征分布，从而显著化训练数据特征中域不变信息；在域通道鉴别模块中，将经过增强前后的特征图输入到域鉴别器中引导模型区分和提取其中的域不变特征，以达到提高模型泛化能力的目的。最后，我们在两个目标数据集进行实验，其结果验证了所提出方法的可行性和有效性。 |
| 中文关键词:跨域少样本图像分类 少样本学习 域泛化 深度学习 |
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| Domain-Channel Knowledge Discriminative Network For Cross-domain Few-shot Image Classification |
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| Abstract:Abstract: Existing cross-domain few-shot classification models are limited by the interference of domain-specific factors, resulting in poor cross-domain performance. To overcome this problem, we propose a Channel Knowledge Discriminative Network for cross-domain few-shot image classification. Specifically, the proposed learning framework contains a stochastic Gaussian affine module and a channel knowledge discrimination module. In the stochastic gaussian affine module, we salientise the domain-invariant information in the feature map by Gaussian perturbing sufficient statistics of the features to generate a new feature distribution that is distinct from the source domain data distribution; In the channel knowledge discrimination module, the feature maps before and after enhancement are fed into the domain discriminator to guide the model to distinguish and extract the domain-invariant features therein, thus improving the model generalisation capability. Finally, we conduct experiments on two target datasets and the results validate the effectiveness of the proposed method. |
| keywords:Cross-domain Few-shot Image Classification Few-Shot Learning Domain Generalisation Deep learning |
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