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평가표

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보존된 평가 기록

1

코드

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

https://github.com/code-implementation1/Code2/tree/main/ArbitraryStyleTransfer

Exploring the structure of a real-time, arbitrary neural artistic stylization network

https://github.com/code-implementation1/Code3/tree/main/ESRGAN

Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

https://github.com/code-implementation1/Code4/tree/main/HRNetV2

Lite-HRNet: A Lightweight High-Resolution Network

https://github.com/code-implementation1/Code4/tree/main/HRNetW48_cls

Lite-HRNet: A Lightweight High-Resolution Network

https://github.com/code-implementation1/Code6/tree/main/Non_local

Non-local Neural Networks

https://github.com/code-implementation1/Code6/tree/main/PAMTRI

PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data

https://github.com/code-implementation1/Code6/tree/main/ras

Recurrent Back-Projection Network for Video Super-Resolution

https://github.com/code-implementation1/Code7/tree/main/RefineNet

RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

https://github.com/code-implementation1/Code8/tree/main/SinGAN

SinGAN-Seg: Synthetic training data generation for medical image segmentation

https://github.com/code-implementation1/Code8/tree/main/simple_baselines

Simple Baselines for Human Pose Estimation and Tracking

https://github.com/code-implementation1/Code8/tree/main/simple_pose

Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation

https://github.com/code-implementation1/Code9/tree/main/EDSR

BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

https://github.com/code-implementation1/Code9/tree/main/Efficientnet

EfficientHRNet: Efficient Scaling for Lightweight High-Resolution Multi-Person Pose Estimation

https://github.com/code-implementation1/Code9/tree/main/MaskRCNN/maskrcnn_mobilenetv1

Mask R-CNN

https://github.com/code-implementation1/Code9/tree/main/ShuffleNet

Slimmable Neural Networks

https://github.com/code-implementation1/Code9/tree/main/hrnet

HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation

https://github.com/codehacpj/Image_description

Show and Tell: A Neural Image Caption Generator

https://github.com/cokowpublic/Nuclei-Segmentation

Mask R-CNN

https://github.com/colemiller94/gatedgan

Gated-GAN: Adversarial Gated Networks for Multi-Collection Style Transfer

https://github.com/collabora/whisperspeech

Simple and Controllable Music Generation

https://github.com/collectionslab/Omniscribe

Mask R-CNN

https://github.com/collectionslab/annotations-computervision

Mask R-CNN

https://github.com/collectionslab/book-annotation-classification

Mask R-CNN

https://github.com/compvis/fm-boosting

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/compvis/imagebart

ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis

https://github.com/compvis/stable-diffusion

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/conceptofmind/Region-ViT-flax

RegionViT: Regional-to-Local Attention for Vision Transformers

https://github.com/copperwire/neural_ode

Neural Ordinary Differential Equations

https://github.com/coulsonlee/dy-dca-eccv2024

Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting

https://github.com/cristinecosta/CompVis

A Style-Aware Content Loss for Real-time HD Style Transfer

https://github.com/cross-domain-compositing/cross-domain-compositing

eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers

https://github.com/crowdai/crowdai-mapping-challenge-mask-rcnn

Mask R-CNN

https://github.com/cryu854/ArbitraryStyle-tfjs

A Neural Algorithm of Artistic Style

https://github.com/cryu854/FastStyle

A Neural Algorithm of Artistic Style

https://github.com/cryu854/SinGAN

Instance Normalization: The Missing Ingredient for Fast Stylization

https://github.com/cscss/DBVSR

Deep Blind Video Super-resolution

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