# Fused Node-Level Residual Structure Edge Graph Neural Network for Few-Shot Image Classification
## 3 Methods
### EGNN + Node Hierarchical Residual Model Architecture
### Training

The suggested EGNN + Node Hierarchical Residual parameters \(\theta_{emb}\cup\left\{\theta_{\
u}^{l},\theta_{e}^{l}\right\}_{l=1}^{L}\) are taught end-to-end by minimizing the following loss function given \(M\) training tasks, denoted as \(\left\{T_{m}^{train}\right\}_{m=1}^{M}\), training at a certain iteration of the scenario training. \[L=\sum\limits_{l=1}^{L}\sum\limits_{m=1}^{M}\lambda_{l}L_{e}\left(Y_{m,e}, \hat{Y}_{m,e}^{l}\right) \tag{8}\]

For the m-th task in the first layer, \(Y_{m,e}\) and \(\hat{Y}_{m,e}^{l}\) represent the sets of all true query edge labels and all (real-valued) query edge predictions, respectively. The binary cross-entropy loss is used to determine the edge loss \(L_{e}\). The total loss includes the losses calculated from all levels to increase gradient flow to lower layers because edge predictions can be acquired not only from the final layer but also from other layers. ## 4 Experiments

### Datasets

**Mini-ImageNet**: Mini-ImageNet is one of the most well-liked benchmark datasets for few-shot learning. It was developed by [PERSON] et al., contains a subset of ImageNet classes, and was chosen at random from the ImageNet ILSVRC-12 [34] dataset. Each of the 100 classes in mini-ImageNet has 600 image examples. The images are color and 84 \(\times\) 84 pixels in size. The training set, validation set, and test set are the three segments that make up the mini-ImageNet dataset. There are 64 classes in the training set, 16 classes in the validation set, and 20 classes in the test set [6]. **tiered-ImageNet**[34]: In contrast to mini-ImageNet, tiered-ImageNet [35] is a subset of the ILSVRC-12 [34] dataset that contains additional categories. A total of 608 categories are included in tiered-ImageNet, and each category has an average of 1281 color photos that are 84 \(\times\) 84 in size. Tiered-ImageNet, as opposed to mini-ImageNet, uses a tiered classification framework for a wider range of categories, which correlate to higher-level nodes in ImageNet. The 34 top-level categories of tiered-ImageNet are further broken down into 20 training categories (351 total categories), 6 validation categories (97 total categories), and 8 test categories (160 total categories). Since categories frequently form hierarchical structures in real-life situations, where high-level nodes cover numerous low-level nodes, this hierarchical design brings tiered-ImageNet closer to problems involving real-world classification. Similar to mini-ImageNet, tiered-ImageNet is a benchmark dataset for few-shot learning that can be used to assess and enhance the performance of few-shot learning algorithms. Tiered-ImageNet can more accurately reflect the complexity and variety of real-world classification problems because of its increased number of categories and samples, making it more extensively relevant for studies in few-shot learning and meta-learning [33; 36]. **The CBAC (Car Brand Appearance Classification)**: A tiny dataset called CBAC (Car Brand Appearance Classification) was created for this study's comparison of the performance of few-shot classification. The classification of few-shot images is its main application. The photographs in this collection were obtained from the Autohome website. There are 22 categories in CBAC, and each category features exterior images of various car brands. All of the photographs are in color and have an 84 \(\times\) 84 pixel dimension. There are 20,254 photos in the CBAC collection. The training set, validation set, and test set are the three segments that make up the CBAC dataset. There are a total of 13,834 photos in the training set, divided among 14 categories, with roughly 1000 images each category. There are roughly 800 photographs each category in both the validation set and test set's 4 categories, for a total of 3210 images in each set. ### Experimental Setup

**Network Architecture:** Similar to the majority of few-shot learning models, a Convolutional Neural Network with four blocks is utilized for the feature embedding module.