Contrastive Augmented Graph2Graph Memory Interaction for Few Shot Continual Learning

2024-03-07 · Biqing Qi, Junqi Gao, Xingquan Chen, Dong Li, Jianxing Liu, Ligang Wu, BoWen Zhou

Few-Shot Class-Incremental Learning (FSCIL) has gained considerable attention in recent years for its pivotal role in addressing continuously arriving classes. However, it encounters additional challenges. The scarcity of samples in new sessions intensifies overfitting, causing incompatibility between the output features of new and old classes, thereby escalating catastrophic forgetting. A prevalent strategy involves mitigating catastrophic forgetting through the Explicit Memory (EM), which comprise of class prototypes. However, current EM-based methods retrieves memory globally by performing Vector-to-Vector (V2V) interaction between features corresponding to the input and prototypes stored in EM, neglecting the geometric structure of local features. This hinders the accurate modeling of their positional relationships. To incorporate information of local geometric structure, we extend the V2V interaction to Graph-to-Graph (G2G) interaction. For enhancing local structures for better G2G alignment and the prevention of local feature collapse, we propose the Local Graph Preservation (LGP) mechanism. Additionally, to address sample scarcity in classes from new sessions, the Contrast-Augmented G2G (CAG2G) is introduced to promote the aggregation of same class features thus helps few-shot learning. Extensive comparisons on CIFAR100, CUB200, and the challenging ImageNet-R dataset demonstrate the superiority of our method over existing methods.

📄 PDF Abstract BibTeX arXiv:2403.04140

Code (0)

등록된 구현이 없습니다.

Tasks

class-incremental learning Class Incremental Learning Continual Learning Few-Shot Class-Incremental Learning Few-Shot Learning Incremental Learning

Subgraph Networks Based Contrastive Learning

2023-06-06 · Jinhuan Wang, Jiafei Shao, Zeyu Wang, Shanqing Yu 외

Graph contrastive learning (GCL), as a self-supervised learning method, can solve the problem of annotated data scarcity. It mines explicit features in unannotated graphs to generate favorable graph representations for d…

Attribute Contrastive Learning Mutual Information Estimation Self-Supervised Learning+1

POI Recommendation with LLM-Augmented Multi-Graph Learning and Contrastive Alignment

2026-08-17 · Burak Tamer, Wolfram Höpken, Zehui Wang arxiv

Point-of-interest (POI) recommendation models based on graph neural networks achieve strong performance by propagating collaborative signals over user-item interactions, yet they struggle with the cold-start problem, whe…

Multimodal Recommendation Collaborative Filtering Graph Neural Network Contrastive Learning

Enhancing Sequential Recommendation with Graph Contrastive Learning

2022-05-30 · Yixin Zhang, Yong liu, Yonghui Xu, Hao Xiong 외

The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exploit the local context information of an…

Auxiliary Learning Contrastive Learning Recommendation Systems Sequential Recommendation

Dual-Channel Latent Factor Analysis Enhanced Graph Contrastive Learning for Recommendation

2024-08-09 · Junfeng Long, Hao Wu

Graph Neural Networks (GNNs) are powerful learning methods for recommender systems owing to their robustness in handling complicated user-item interactions. Recently, the integration of contrastive learning with GNNs has…

Contrastive Learning Recommendation Systems

QoS-Aware Graph Contrastive Learning for Web Service Recommendation

2024-01-06 · Jeongwhan Choi, Duksan Ryu

With the rapid growth of cloud services driven by advancements in web service technology, selecting a high-quality service from a wide range of options has become a complex task. This study aims to address the challenges…

Contrastive Learning