J J Li's Projects
Cycle-consistent Conditional Adversarial Transfer Networks, ACM MM 2019
source code for Divergence-agnostic Unsupervised Domain Adaptation by Adversarial Attacks in TPAMI
Adversarial Energy Disaggregation
Alleviating Feature Confusion for Generative Zero-shot Learning, ACM MM 2019
Maximum Density Divergence for Domain Adaptation, TPAMI 2020, Code release, Cross-domain Adversarial Tight Match
Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)
Car Recognition with Deep Learning
Codes for "Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation" in CVPR 2021
Code for "Coupled local–global adaptation for multi-source transfer learning" published on Neurocomputing
Code for the ECCV 2018 paper "Pairwise Confusion for Fine-Grained Visual Classification"
Unsupervised Domain Adaptation Papers and Code
人人可用的开源数据可视化分析工具。
A paper list of object detection using deep learning.
fire-smoke-detect-yolov4-yolov5 and fire-smoke-detection-dataset 火灾检测,烟雾检测
Tools for capturing and analysing keyboard input paired with microphone capture
Heterogeneous domain adaptation through progressive alignment, TNNLS
Dr. Jingjing Li's Personal Website, UESTC
LisGAN, Leveraging the Invariant Side of Generative Zero-Shot Learning, CVPR 2019
Low-Rank Linear Autoencoder, LLAE, AAAI 2019, From Zero-Shot Learning to Cold-Start Recommendation
Source code for Learning Modality-Invariant Latent Representations for Generalized Zero-shot Learning, ACM MM
Locality Preserving Joint Transfer for Domain Adaptation, TIP 2019
Code for "Low-rank discriminant embedding for multiview learning" published on IEEE TCYB
Transfer Learning for Multimedia Applications (Special Issue on Multimedia Tools and Applications)
A toolbox for domain adaptation and semi-supervised learning. Contributions welcome.
Code for "Transfer Independently Together: A Generalized Framework for Domain Adaptation" published on IEEE TCYB
Everything about Transfer Learning and Domain Adaptation--迁移学习
基于vue2+vuex+router+echarts的数据可视化大屏,使用缩放进行了屏幕的适配