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Adversarial Feature Augmentation for Cross-domain Few-shot Classification To address this issue, we propose a novel adversarial feature augmentation (AFA) method to bridge the domain gap in few-shot learning The feature augmentation is designed to simulate distribution variations by maximizing the domain discrepancy
Adversarial Feature Augmentation for Cross-domain Few-shot Classification To overcome the limitations in existing works, we propose a novel adversar-ial feature augmentation (AFA) method for domain-invariant feature learning in cross-domain few-shot classification Diferent from the ATA, our method per-forms data augmentation in features (instead of tasks) to simulate the feature dis-tribution mismatch across domains
Adversarial Feature Augmentation for Cross-domain Few . . . 在对抗性训练中，通过区分增强的特征（未见域）和原始特征（见域）来学习域区分器，同时域差异被最小化以获得最佳特征 编码器。 所提出的方法是一个即插即用的模块，可以很容易地集成到现有的基于元学习的小样本学习方法中。 在9个数据集上的广泛实验表明，与目前的技术水平相比，所提出方法在跨域的小样本分类方面具有优势。 已开源： github com youthhoo AFA 论文： Euler Systems and Selmer Bounds for GU (2,1) 现有的基于元学习的方法通过从基础类的（源域）训练任务中学习的元知识来预测（目标域）测试任务的新类标签。 然而，由于不同领域之间可能存在较大的差异，大多数现有的工作可能无法推广到新的类别。 为了解决这个…
跨域小样本---cross domain few shot---第一弹-CSDN博客 Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation（ICLR 2020 link） 核心思想: 在网络后的BatchNorm后添加特征变换层，对来自BatchNorm的特征进行扰动增强（会增加一个高斯分布进来），以此提高模型对不同域的适配性。
FADS: Fourier-Augmentation Based Data-Shunting for Few-Shot Classification Abstract: Collecting a substantial number of labeled samples is infeasible in many real-world scenarios, thereby bringing out challenges for supervised classification The research on Few-Shot Classification (FSC) aims to address this issue
GitHub - youthhoo AFA_For_Few_shot_learning 1 Test the trained model on the unseen domains 2 Fine-tuning with linear classifier To get the results of traditional pre-training and fine-tuning, run 3 Fine-tuning the meta-learning models This code is built upon the implementation from CloserLookFewShot, CrossDomainFewShot, CDFSL-ATA, cdfsl-benchmark, few-shot-lrp-guided and TPN-pytorch
FADS: Fourier-Augmentation Based Data-Shunting for Few-Shot. . . FADS mainly contains two operations, namely Fourier-based data augmentation (FDA) and data shunting (i) Fourier transform has a desirable property for classification tasks: the image’s phase and amplitude components in the frequency domain correspond to its high-level structure (i e , semantic) and low-level style (i e , statistic
Cross-Domain Few-Shot Classification via Adversarial Task Augmentation Our method can be used as a simple plug-and-play module for various meta-learning models, and improve their cross-domain generalization capability We conduct extensive experiments under the cross-domain setting, using nine few-shot classification datasets: mini-ImageNet, CUB, Cars, Places, Plantae, CropDiseases, EuroSAT, ISIC and ChestX
Cross-Domain Few-Shot Classification via Adversarial Task Augmentati pability of meta-learning models in the cross-domain few-shot learning For this, we consider the worst-case problem around the source task dis-tribution T0, and propose a plug-and-play inductive bias-adaptive task augmentation method, which significantly im-proves the cross-domain few-shot classification capabil
FAD: Frequency Adaptation and Diversion for Cross-domain Few-shot Learning FAD operates during meta-testing and incorporates multiple spectral branches to capture frequency-specific variations At the core of FAD is the Frequency Diversion Adapter, a lightweight residual module that transforms features into the frequency domain via discrete Fourier transform and partitions them into predefined bands using spectral masks