Search Results for author: Yiying Li

Found 12 papers, 2 papers with code

MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification

no code implementations17 Sep 2023 Junjie Zhu, Yiying Li, Chunping Qiu, Ke Yang, Naiyang Guan, Xiaodong Yi

In order to tackle these issues, we turn to the recently proposed parameter-efficient tuning methods, such as VPT, which updates only the newly added prompt parameters while keeping the pre-trained backbone frozen.

Data Augmentation Domain Adaptation +4

Dynamic Memory-based Curiosity: A Bootstrap Approach for Exploration

no code implementations24 Aug 2022 Zijian Gao, Yiying Li, Kele Xu, Yuanzhao Zhai, Dawei Feng, Bo Ding, XinJun Mao, Huaimin Wang

The curiosity arouses if memorized information can not deal with the current state, and the information gap between dual learners can be formulated as the intrinsic reward for agents, and then such state information can be consolidated into the dynamic memory.

Reinforcement Learning (RL)

Nuclear Norm Maximization Based Curiosity-Driven Learning

no code implementations21 May 2022 Chao Chen, Zijian Gao, Kele Xu, Sen yang, Yiying Li, Bo Ding, Dawei Feng, Huaimin Wang

To handle the sparsity of the extrinsic rewards in reinforcement learning, researchers have proposed intrinsic reward which enables the agent to learn the skills that might come in handy for pursuing the rewards in the future, such as encouraging the agent to visit novel states.

Atari Games

A Fixed Version of Quadratic Program in Gradient Episodic Memory

no code implementations7 Jul 2021 Wei Zhou, Yiying Li

Gradient Episodic Memory is indeed a novel method for continual learning, which solves new problems quickly without forgetting previously acquired knowledge.

Continual Learning

KnowSR: Knowledge Sharing among Homogeneous Agents in Multi-agent Reinforcement Learning

no code implementations25 May 2021 Zijian Gao, Kele Xu, Bo Ding, Huaimin Wang, Yiying Li, Hongda Jia

In this paper, we present an adaptation method of the majority of multi-agent reinforcement learning (MARL) algorithms called KnowSR which takes advantage of the differences in learning between agents.

Knowledge Distillation Multi-agent Reinforcement Learning +2

KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learning

no code implementations27 Mar 2021 Zijian Gao, Kele Xu, Bo Ding, Huaimin Wang, Yiying Li, Hongda Jia

In this paper, we propose a method, named "KnowRU" for knowledge reusing which can be easily deployed in the majority of the multi-agent reinforcement learning algorithms without complicated hand-coded design.

Knowledge Distillation Multi-agent Reinforcement Learning +2

FedH2L: Federated Learning with Model and Statistical Heterogeneity

no code implementations27 Jan 2021 Yiying Li, Wei Zhou, Huaimin Wang, Haibo Mi, Timothy M. Hospedales

Federated learning (FL) enables distributed participants to collectively learn a strong global model without sacrificing their individual data privacy.

Federated Learning

Attention-based Fault-tolerant Approach for Multi-agent Reinforcement Learning Systems

no code implementations5 Oct 2019 Mingyang Geng, Kele Xu, Yiying Li, Shuqi Liu, Bo Ding, Huaimin Wang

The aim of multi-agent reinforcement learning systems is to provide interacting agents with the ability to collaboratively learn and adapt to the behavior of other agents.

Multi-agent Reinforcement Learning reinforcement-learning +1

Feature Fusion Detector for Semantic Cognition of Remote Sensing

no code implementations28 Sep 2019 Wei Zhou, Yiying Li

Based on experiments on the remote sensing dataset from Google Earth, our LFFN has proved effective and practical for the semantic cognition of remote sensing, achieving 89% mAP which is 4. 1% higher than that of FPN.

Feature-Critic Networks for Heterogeneous Domain Generalization

2 code implementations31 Jan 2019 Yiying Li, Yongxin Yang, Wei Zhou, Timothy M. Hospedales

The well known domain shift issue causes model performance to degrade when deployed to a new target domain with different statistics to training.

Domain Generalization

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