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2D Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Marcos14Almeida/darknet
https://github.com/MargeryLab/Pytorch-UNet-copy
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/MariefW/6.-trainingYOLO
https://github.com/MarioNavarrete/anchor_optimization
Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels
https://github.com/Marior87/Telegram_Bot
https://github.com/MarkHershey/arxiv-dl
https://github.com/MartinGer/Attention-Augmented-Convolutional-Networks
https://github.com/MartinGer/Bottleneck-Transformers-for-Visual-Recognition
https://github.com/MartinGer/Stand-Alone-Self-Attention-in-Vision-Models
https://github.com/MarvinLavechin/imagetranslation-tensorflow
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/Mary-xl/HRnet_Kaggle_iNat2019_FGVC
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/Masao-Someki/CycleVAE_VC
Non-Parallel Voice Conversion with Cyclic Variational Autoencoder
https://github.com/Masao-Taketani/FOTS_OCR
https://github.com/Maskify/darknet
https://github.com/MasoumehVahedi/Yolo-Object-Detection
https://github.com/MattSegal/speech-enhancement
Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation
https://github.com/MatthewQi2002/Unet_mindspore
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Maveric4/pr19aaw01
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/MaxLikesMath/Barlow-Twins-Pytorch
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
https://github.com/Maxencephilbert/In_Silico_Labeling_CBIO_internship
https://github.com/MaximKuklin/3D_Object_Detection_Diploma
https://github.com/MaximeBataille/tennis_tracking
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/FastNeRF_High_Fidelity_Neural_Rendering_at_200FPS
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/FreeNeRF_Improving_Few_shot_Neural_Rendering_with_Free_Frequency_Regularization
FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Instant_Neural_Graphics_Primitives_with_a_Multiresolution_Hash_Encoding
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Model_Agnostic_Meta_Learning_for_Fast_Adaptation_of_Deep_Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/NeRF_Representing_Scenes_as_Neural_Radiance_Fields_for_View_Synthesis
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/PlenOctrees_for_Real_time_Rendering_of_Neural_Radiance_Fields
PlenOctrees for Real-time Rendering of Neural Radiance Fields
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Unpaired_Image_to_Image_Translation_using_Cycle_Consistent_Adversarial_Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/MaxwellHeller/coffee_fraud_detection
https://github.com/MayMiao0923/PESQ
MOSNet: Deep Learning based Objective Assessment for Voice Conversion
https://github.com/MayankGupta73/Workout-Evaluation-tf-openpose
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/Mayurji/Image-Classification-PyTorch
https://github.com/McDo/LightFlowPytorch
https://github.com/MdAli1993/Multiclass_Semantic_Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.