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liguihong's Projects

attentivenas icon attentivenas

code for "AttentiveNAS Improving Neural Architecture Search via Attentive Sampling"

autocodegenerator icon autocodegenerator

A winforms code generator for c#/sql/asp projects - simple, lightweight, and fast

capsnet-tensorflow icon capsnet-tensorflow

A Tensorflow implementation of CapsNet(Capsules Net) in Hinton's paper Dynamic Routing Between Capsules

carp icon carp

The implementation of “A Capsule Network for Recommendation and Explaining What You Like and Dislike”, Chenliang Li, Cong Quan, Li Peng, Yunwei Qi, Yuming Deng, Libing Wu, https://dl.acm.org/citation.cfm?doid=3331184.3331216

damo-yolo icon damo-yolo

DAMO-YOLO: a fast and accurate object detection method with some new techs, including NAS backbones, efficient RepGFPN, ZeroHead, AlignedOTA, and distillation enhancement.

darts icon darts

Differentiable architecture search for convolutional and recurrent networks

deepbench icon deepbench

Benchmarking Deep Learning operations on different hardware

deepcaps icon deepcaps

Official Implementation of "DeepCaps: Going Deeper with Capsule Networks" paper (CVPR 2019).

deepfacelab icon deepfacelab

DeepFaceLab is the leading software for creating deepfakes.

deepfakes icon deepfakes

This is the code for "DeepFakes" by Siraj Raval on Youtube

faceit icon faceit

A script to make it easy to swap faces in videos using the faceswap library, and YouTube videos.

fairseq icon fairseq

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

gem5 icon gem5

This is an read-only mirror of the gem5 simulator. The upstream repository is stored in https://gem5.googlesource.com, code reviews should be submitted to https://gem5-review.googlesource.com/. The mirrors are synchronized every 15 minutes.

graph2nn-1 icon graph2nn-1

code for paper "Graph Structure of Neural Networks"

h3 icon h3

Language Modeling with the H3 State Space Model

heinsen_routing icon heinsen_routing

Official implementation of "An Algorithm for Routing Capsules in All Domains" (Heinsen, 2019) in PyTorch.

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