Fundamental & Deep Learning (FDL) Team

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

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

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

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

Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member Models

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

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

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