Papers with Code 아카이브

2D Classification

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

https://github.com/Dmitrsl/Tools

Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/DmitryRyumin/ICCV-2023-Papers

Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement

https://github.com/DmitryUlyanov/fast-neural-doodle

A Neural Algorithm of Artistic Style

https://github.com/DoTung-bkhn/multiclass-segmentation

Pyramid Scene Parsing Network

https://github.com/Dodant/ANPR-with-Yolov4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/DogeZen/resnet2onnx2tensorrt

Deep Residual Learning for Image Recognition

https://github.com/DominickZhang/Distillation-Swin-Transformer

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/DonCorle0ne/damage_detection

YOLO9000: Better, Faster, Stronger

https://github.com/Dong1P/MTRNN

Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training

https://github.com/Donvink/Car_Detection_for_Autonomous_Driving

You Only Look Once: Unified, Real-Time Object Detection

https://github.com/DoranLyong/SPANet-official

Spectral-Adaptive Modulation Networks for Visual Perception

https://github.com/Doyosae/Deep-Complex-Networks

On Complex Valued Convolutional Neural Networks

https://github.com/Doyosae/Deep_Complex_Networks

On Complex Valued Convolutional Neural Networks

https://github.com/Doyosae/Stand-Alone-Self-Attention

Stand-Alone Self-Attention in Vision Models

https://github.com/DrMMZ/ProtoNet

Prototypical Networks for Few-shot Learning

https://github.com/DrMMZ/RetinaNet

Focal Loss for Dense Object Detection

https://github.com/Dragoniss/mindspore-phase2-Deep-Fourier-Upsampling/blob/main/Fourier-Upsampling.py

Deep Fourier Up-Sampling

https://github.com/DreamtaleCore/Refool

Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks

https://github.com/DuaneNielsen/keypoints

Unsupervised Learning of Object Keypoints for Perception and Control

https://github.com/DucQuan12/Classification-with-activation-mish

Mish: A Self Regularized Non-Monotonic Activation Function

https://github.com/Duplums/bhb10k-dl-benchmark

Deep Residual Learning for Image Recognition

https://github.com/Dycollapsar/Attention-Based-for-Medicalimaging

Squeeze-and-Excitation Networks

https://github.com/Dylan-get/Deep-Feature-Consistent-VAE

Deep Feature Consistent Variational Autoencoder

https://github.com/E-DEEP/PapersReview

Zero-Shot Instance Segmentation

https://github.com/ECE740F21T01/pytorch-pose-hg-3d

Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach

https://github.com/EMI-Group/FaPN

Feature Pyramid Networks for Object Detection

https://github.com/EMI-Group/TransferLearning-Tasks

RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning

https://github.com/EMalagoli92/GCViT-TensorFlow

Global Context Vision Transformers

https://github.com/EMalagoli92/VAN-Classification-TensorFlow

Visual Attention Network

https://github.com/EMsnap/RobotSorting

YOLO9000: Better, Faster, Stronger

https://github.com/ENSTA-U2IS/MUAD-Dataset

MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks

https://github.com/EPFL-VILAB/3DCommonCorruptions

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/EPFL-VILAB/XDEnsembles

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ESBigeard/paper_graph

BioSentVec: creating sentence embeddings for biomedical texts

https://github.com/EXJUSTICE/Cross-Domain_Classification_Using_StyleTransfer

Cross-Domain Image Classification through Neural-Style Transfer Data Augmentation

https://github.com/EbadSyed/spadRGBD

Fast Robust Monocular Depth Estimation for Obstacle Detection with Fully Convolutional Networks

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