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2D Classification

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/PARC/intricate-art-neural-transfer

A Neural Algorithm of Artistic Style

https://github.com/PB17151764/2020UM-Summer-Research

Deep Retinal Image Understanding

https://github.com/PDEUXA/AIF_CYCLEGAN

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/PINTO0309/PINTO_model_zoo/tree/main/326_YOLOPv2

YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception

https://github.com/PKU-TANGENT/nlp-tutorial

Generating News Headlines with Recurrent Neural Networks

https://github.com/POSTECH-CVLab/FastPointTransformer

Point Transformer

https://github.com/POSTECH-CVLab/point-transformer

Point Transformer

https://github.com/PPPrior/i3d-pytorch

Deep Residual Learning for Image Recognition

https://github.com/PPPrior/u2net

U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection

https://github.com/PRBonn/bonnet

Pyramid Scene Parsing Network

https://github.com/PaParaZz1/TemporalShiftModule

TSM: Temporal Shift Module for Efficient Video Understanding

https://github.com/PacktPublishing/Hands-On-Image-Generation-with-TensorFlow-2.0/tree/master/Chapter04

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/PacktPublishing/Hands-On-Image-Generation-with-TensorFlow-2.0/tree/master/Chapter05

A Neural Algorithm of Artistic Style

https://github.com/PaddlePaddle/PASSL

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

https://github.com/PaddlePaddle/PASSL/blob/main/docs/Train_CLIP_model.md

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/PaddlePaddle/PLSC

ConvMAE: Masked Convolution Meets Masked Autoencoders

https://github.com/PaddlePaddle/PLSC/tree/master/task/classification/swin

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/PaddlePaddle/PLSC/tree/master/task/ssl/cae

Context Autoencoder for Self-Supervised Representation Learning

https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dino

DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/semi_det

Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection

https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/smalldet

Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection

https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/vitdet

Exploring Plain Vision Transformer Backbones for Object Detection

https://github.com/PaddlePaddle/PaddleFL

Context Autoencoder for Self-Supervised Representation Learning

https://github.com/PaddlePaddle/PaddleMIX

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

https://github.com/PaddlePaddle/PaddleMIX/tree/develop/paddlemix/examples

EVA: Exploring the Limits of Masked Visual Representation Learning at Scale

https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/gpt-3

Language Models are Few-Shot Learners

https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/transformer-xl

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

https://github.com/PaddlePaddle/PaddleOCR

Synthetic Data for Text Localisation in Natural Images

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/maml

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

https://github.com/PaddlePaddle/PaddleSeg

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/PaddlePaddle/PaddleSpeech

Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis

https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/pp-tsm.md

TSM: Temporal Shift Module for Efficient Video Understanding

https://github.com/PaddlePaddle/PaddleYOLO

You Only Look One-level Feature

https://github.com/PaddlePaddle/VIMER

Context Autoencoder for Self-Supervised Representation Learning

https://github.com/PaddlePaddle/models

Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/PaiHsuehChung/DockerYOLOV3

YOLOv3: An Incremental Improvement

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.