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PL's Projects

learntopayattention icon learntopayattention

PyTorch implementation of ICLR 2018 paper Learn To Pay Attention (and some modification)

leetcode-1 icon leetcode-1

:pencil: Python / C++ 11 Solutions of LeetCode Questions

lip_jppnet icon lip_jppnet

Code repository for Joint Body Parsing & Pose Estimation Network, T-PAMI 2018

lip_ssl icon lip_ssl

Code repository for Self-supervised Structure-sensitive Learning, CVPR'17

llama-adapter icon llama-adapter

Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters

llava icon llava

Large Language-and-Vision Assistant built towards multimodal GPT-4 level capabilities.

lrr icon lrr

code and models for "Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation"

lsfm icon lsfm

Large Scale Facial Model (LSFM) - an automatic pipeline for constructing 3D Morphable Models from large collections of facial meshes

ltc-msda icon ltc-msda

Implementation of Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation (ECCV 2020).

madan icon madan

Pytorch Code release for our NeurIPS paper "Multi-source Domain Adaptation for Semantic Segmentation"

malsar icon malsar

Multi-task learning via Structural Regularization

maml icon maml

Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"

maskrcnn-benchmark icon maskrcnn-benchmark

Fast, modular reference implementation of Semantic Segmentation and Object Detection algorithms in PyTorch.

mayo icon mayo

Mayo: Auto-generation of hardware-friendly deep neural networks. Dynamic Channel Pruning: Feature Boosting and Suppression.

mdda icon mdda

Implementation of paper multi-source distilling domain adaptation

mdm icon mdm

A TensorFlow implementation of the Mnemonic Descent Method.

mean-teacher icon mean-teacher

A state-of-the-art semi-supervised method for image recognition

megatron-lm icon megatron-lm

Ongoing research training transformer models at scale

mentornet icon mentornet

Code for MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks

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