Saliency-Aware Regularized Graph Neural Network

no code implementations • 1 Jan 2024 • Wenjie Pei, Weina Xu, Zongze Wu, Weichao Li, Jinfan Wang, Guangming Lu, Xiangrong Wang

In this work, we propose the Saliency-Aware Regularized Graph Neural Network (SAR-GNN) for graph classification, which consists of two core modules: 1) a traditional graph neural network serving as the backbone for learning node features and 2) the Graph Neural Memory designed to distill a compact graph representation from node features of the backbone.

Graph Classification Representation Learning +2

Progressive Multi-Stage Learning for Discriminative Tracking

no code implementations • 1 Apr 2020 • Weichao Li, Xi Li, Omar Elfarouk Bourahla, Fuxian Huang, Fei Wu, Wei Liu, Zhiheng Wang, Hongmin Liu

Visual tracking is typically solved as a discriminative learning problem that usually requires high-quality samples for online model adaptation.

Visual Tracking

State Distribution-aware Sampling for Deep Q-learning

no code implementations • 23 Apr 2018 • Weichao Li, Fuxian Huang, Xi Li, Gang Pan, Fei Wu

A critical and challenging problem in reinforcement learning is how to learn the state-action value function from the experience replay buffer and simultaneously keep sample efficiency and faster convergence to a high quality solution.

Atari Games OpenAI Gym +1