queenie88 Goto Github PK

k-gnn

Source code for our AAAI paper "Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks".

kaggle-ndsb

Winning solution for the National Data Science Bowl competition on Kaggle (plankton classification)

kaggle_forest_2017

kcnet_pytorch

Partial implemention of KCNet

kdgan

keras_learning

knowledge-distillation-pytorch

A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility

knowledgesharing-pytorch

Implementations of knowledge distillation and knowledge transfer models in neural networks.

kpm_rw_person_reid

Code of Deep Kronecker-Product Matchingand Group-shuffiling Random Walk for Person Re-ID

l-dnq

Codes for AAAI2019 paper: Deep Neural Network Quantization via Layer-Wise Optimization using Limited Training Data

l-gm-loss

Implementation of our accepted CVPR 2018 paper "Rethinking Feature Distribution for Loss Functions in Image Classification"

l2g-ae

L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention

label-embedding-network

Label Embedding Network

label_smoothing

Corrupted labels and label smoothing

labelnoisecorrection

Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction

lamp

Graph Neural Networks for Multi-Label Classification

latent-noise-icnm

Code for CVPR 2016 paper on Learning from Noisy Labels

leam

learncode

方便学习的代码仓库

learning-similarity-conditions

learning-to-reweight-examples

PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning

learning-to-reweight-examples-1

Code for paper "Learning to Reweight Examples for Robust Deep Learning"

learning3d

This is a complete package of recent deep learning methods for 3D point clouds in pytorch (with pretrained models).

learning_by_grad_by_grad_repro

Reproduction of "Learning to Learn by Gradient Descent by Gradient Descent"

learningtocompare_fsl

PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)

learningtocompare_zsl

PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Zero-Shot Learning part)

learntrajdep

code for learning trajectory dependencies for human motion prediction

light-weight-refinenet

Light-Weight RefineNet for Real-Time Semantic Segmentation

lightmbn

limit-label-memorization

Improving generalization by controlling label-noise information in neural network weights.