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This repository provides hands-on labs on PyTorch-based Distributed Training and SageMaker Distributed Training. It is written to make it easy for beginners to get started, and guides you through step-by-step modifications to the code based on the most basic BERT use cases.

A practical field guide for ML researchers or engineers from first CUDA setup to fine-tuning large language models on consumer hardware. Covers PyTorch GPU workflows, mixed precision training, DataLoader optimization, multi-GPU scaling, quantization, and LoRA fine-tuning. Every chapter is built a... Read more

PyTorch code for JSTSP2021 paper "Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network""

PyTorch implementation of A Lightweight Polarization-Guided Plug-in for Underwater Image Enhancement

PyTorch code for our paper "LSGQuant: Layer-Sensitivity Guided Quantization for One-Step Diffusion Real-World Video Super-Resolution"

Pytorch version of the CVPR 2020 paper: Blindly Assess Image Quality in the Wild Guided by A Self-Adaptive Hyper Network

A PyTorch-based optimizer wrapper for continual learning via selective fine-tuning, guided by the condition number κ (kappa) of model tensors. KappaTune identifies and updates only the least anisotropic parameters to preserve pre-trained knowledge and mitigate catastrophic forgetting.

PyTorch implementation of "Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors" (ICLR 2025).

[ICIP2021] The official PyTorch implementation of MASK GUIDED ATTENTION FOR FINE-GRAINED PATCHY IMAGE CLASSIFICATION

Pytorch implementation of CT-CIG from AAAI 2026 paper "Text-guided Controllable Diffusion for Realistic Camouflage Images Generation"