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Name: Fundamental & Deep Learning (FDL) Team, Zhejiang Lab

Type: Organization

Bio: Theoretical and algorithmic research on open-world learning and decision-making problems

Fundamental & Deep Learning (FDL) Team, Zhejiang Lab's Projects

Python code to implement a commonsense knowledge inference module (CKIM) assisted deep learning method for visual object detection

Python code to implement CO3, a fundamental-model based approach for open-world few-shot learning.

Python codes to implement DeMix, a DETR assisted CutMix method for image data augmentation

Code to implement the Runner-up Solution to the CVPR'23 SoccerNet Challenge (Ball Action Spotting Task)

Python code to implement hard sampling based task representation learning for robust offline meta RL

A RL approach to enable cost-effective, intelligent interactions between a local agent and a remote LLM

Python code to implement LLM4Teach, a policy distillation approach for teaching reinforcement learning agents with Large Language Model

Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member Models

Source code of mentioned slot pool

Python codes to implement the PFGE algorithm

Python codes to implement the periodic stochastic weight averaging (PSWA) algorithm

Python codes to implement Q-Net, a meta-learning method for few shot medical image segmentation

Code to implement Restormer-Plus, the Runner-up Solution to the GT-RAIN Challenge (CVPR 2023 UG2+ Track 3)

Python code to implement an efficient approach to unsupervised OOD detection with VAE

WordNet Python Interface