Transductive Relation-Propagation With Decoupling Training for Few-Shot Learning

Graph Neural Networks With Triple Attention for Few-Shot Learning

“…TLRM [7] proposed a sample-to-task relation module to capture the task-level relation representations in each GNN layer. TRPN-D [8] adopted the decoupling training strategy to preserve the diversity across different fewshot tasks to enhance the generalizability of GNN models. GCLR [9] applied a VAE-based encoder-decoder module to enrich the node representations in the latent feature space.…”

“…Compared with CNNs, Graph Networks are more powerful in exploiting the intra-and interclass relationships amongst samples, which are thus more effective for few-shot learning. The current GNN-based fewshot methods improve the accuracy and generalizability from the perspective of node/edge update [8], [9], [12], [13] and graph structure design [5], [14], [15]. In general, graph-based methods model the feature embeddings of samples as vertices in a graph and propagate label information between nodes by performing node or edge feature aggregation from neighbor nodes with graph convolution.…”

Image classification with limited data information

“…In this work, we focus on the third type, namely the metric learning-based methods [44,[59][60][61][62][63][64][65][66][67][68], i.e., to learn the discriminative feature embeddings for distinguishing different image classes. For example, ProtoNet [59] considered the class-mean representation as the prototype of each class and applied the Euclidean distance metric for classification.…”

“…Classic few-shot methods [14,59,61,115] applied Convolutional Neural Networks (CNNs) for image classification. More recent works proposed to apply the Graph Neural Networks [65,[116][117][118] or Graph Convolutional Networks [71,119] to process data with rich relational structures in few-shot scenarios. Compared with CNNs, Graph Networks are more powerful in exploiting the intra-and inter-class relationships amongst samples, which are thus more effective for few-shot learning.…”

“…Compared with CNNs, Graph Networks are more powerful in exploiting the intra-and inter-class relationships amongst samples, which are thus more effective for few-shot learning. The current GNN-based few-shot methods improve the accuracy and generalizability from the perspective of node/edge update [65,118,120,121] and graph structure design [116,122,123]. In general, graph-based methods model the feature embeddings of samples as vertices in a graph and propagate label information between nodes by performing node or edge feature aggregation from neighbor nodes with graph convolution.…”