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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/areffarhadi/mamba-asr

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/areylng/SPADE-MVTEC-LOCO-AD-dataset

Sub-Image Anomaly Detection with Deep Pyramid Correspondences

https://github.com/arghadeep25/Face-Detection

Max-Margin Object Detection

https://github.com/ariG23498/mae-scalable-vision-learners

Masked Autoencoders Are Scalable Vision Learners

https://github.com/ariangc/breinchallenge

Deep Residual Learning for Image Recognition

https://github.com/arifromadhan19/rcnn-pneumonia

Rich feature hierarchies for accurate object detection and semantic segmentation

https://github.com/aritzLizoain/Image-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/arjunakula/neurips2021

CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions

https://github.com/armaank/archlectures

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/armin-azh/3DefficientNet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/arnab39/cycleGAN-PyTorch

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

https://github.com/arnavdodiedo/Neural-Style-Transfer

A Neural Algorithm of Artistic Style

https://github.com/arnavdodiedo/dog2sketch

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

https://github.com/arnavdodiedo/sketch2face

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

https://github.com/arne-cl/rst-workbench

Fast Rhetorical Structure Theory Discourse Parsing

https://github.com/arobey1/advbench

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/arobey1/mbrdl

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/aronfothi/mask_cluster_rcnn

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/arpytanshu/ssd-object-detection

SSD: Single Shot MultiBox Detector

https://github.com/arsenal9971/shearlet_semantic_edge

CASENet: Deep Category-Aware Semantic Edge Detection

https://github.com/arshd91/multitarget-voice-conversion-vctk

Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio Representations

https://github.com/artemmavrin/focal-loss

Focal Loss for Dense Object Detection

https://github.com/arthurbra/stm-corpus/blob/master/README.md

Domain-independent Extraction of Scientific Concepts from Research Articles

https://github.com/arthurflor23/surface-crack-detection

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/artitw/BERT_QA

Question Generation by Transformers

https://github.com/artxtech/darknet-rnn

YOLO9000: Better, Faster, Stronger

https://github.com/artynet/darknet-alexeyAB

YOLO9000: Better, Faster, Stronger

https://github.com/arvind1998/Neural-Style-Transfer

A Neural Algorithm of Artistic Style

https://github.com/arvind1998/SSD-Object-Detection

SSD: Single Shot MultiBox Detector

https://github.com/aryan-mann/style-transfer

A Neural Algorithm of Artistic Style

https://github.com/aryan26roy/Neural-Style-Transfer

A Neural Algorithm of Artistic Style

https://github.com/aryanasadianuoit/MobileNet_V2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/aryanasadianuoit/mobilenet_v1

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/as791/Deep-Generative-Filter-For-Motion-Deblurring-PyTorch

Deep Generative Filter for Motion Deblurring

https://github.com/asad-62/IVP-DNN

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/asahi417/lmppl

Language Models are Few-Shot Learners

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