Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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평가표

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보존된 평가 기록

3

코드

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

https://github.com/samsonadmin/modified-alexeyab-darknet

Focal Loss for Dense Object Detection

https://github.com/samuel500/World-Models

World Models

https://github.com/samuelmat19/DDPG-tf2

Continuous control with deep reinforcement learning

https://github.com/samuelyu2002/PFLD

PFLD: A Practical Facial Landmark Detector

https://github.com/sanantoniochili/PointCloud_KNN

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/sanaullah-ashfat/Face-recognition-with-Arcface-Tensorflow

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/sanaullah-ashfat/Face-recognition-with-Arcface-Tensowflow-

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/sandipsahajoy/Predicting-Lymphoma-using-CNN-in-Keras

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/sangHa0411/CIFAR100

Deep Residual Learning for Image Recognition

https://github.com/sangHa0411/ResNetCIFAR100

Deep Residual Learning for Image Recognition

https://github.com/sanghyun-son/clustering-kernels

Deep Residual Learning for Image Recognition

https://github.com/sangyun884/Face2Webtoon

Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation

https://github.com/sangyun884/WebtoonFaces

Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation

https://github.com/sankn123/Very-deep-convolutional-networks-for-large-scale-image-recognition-Implementation

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/sanoyo/dl-model

Deep Residual Learning for Image Recognition

https://github.com/santolucito/Haskell-TORCS

Simulated Car Racing Championship: Competition Software Manual

https://github.com/saoudh/Reinforcement-Learning

Continuous control with deep reinforcement learning

https://github.com/sarmueller/gibo

Simple random search provides a competitive approach to reinforcement learning

https://github.com/sarthakTUM/roofn3d

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/sarthakbaiswar/Artistic-Styling

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/saschaschramm/Pong

Proximal Policy Optimization Algorithms

https://github.com/say2sarwar/DeepAcid

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/sayakpaul/Adaptive-Gradient-Clipping

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/sayakpaul/Revisiting-Pooling-in-CNNs

RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference

https://github.com/sayannath/Zero-DCE-TFLite

Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement

https://github.com/sayantanauddy/hierarchical_bipedal_controller

Continuous control with deep reinforcement learning

https://github.com/sberbank-ai/DigiTales

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/sberbank-ai/DigiTeller

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/sberbank-ai/real-esrgan

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/sbodenstein/Tutorials

Simple random search provides a competitive approach to reinforcement learning

https://github.com/sc2crazy/StarCrackRL

Proximal Policy Optimization Algorithms

https://github.com/scarlettjiali/Tools_Project

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/schatty/D4PG-pytorch

Continuous control with deep reinforcement learning

https://github.com/scottemmons/youngs-cql

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/scoutant/face-blur

Towards Fast, Accurate and Stable 3D Dense Face Alignment

https://github.com/sdamolini/LooksLikeWho

FaceNet: A Unified Embedding for Face Recognition and Clustering

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