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

2

보존된 평가 기록

3

코드

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

https://github.com/soumik12345/Nearest-Celebrity-Face

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/soumik12345/Twin-Delayed-DDPG

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/soumik12345/Zero-DCE

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

https://github.com/soumik12345/point-cloud-segmentation

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

https://github.com/soumith/convnet-benchmarks

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/souravverma94/pre_trained_imgclassifier_dogBreeds

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/sovit-123/Deep-Learning-Image-Captioning

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/sparkmxy/my-offlinerl

Off-Policy Deep Reinforcement Learning without Exploration

https://github.com/sppsps/VGG-16-classification

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/srihari-humbarwadi/retinanet-tensorflow2.x

Focal Loss for Dense Object Detection

https://github.com/srihari-humbarwadi/tensorflow_retinanet

Focal Loss for Dense Object Detection

https://github.com/srilakshmiv14/FaceNet_Pytorch

Learning Face Representation from Scratch

https://github.com/srvcodes/clap4clip

Learning to Prompt for Vision-Language Models

https://github.com/starhiking/ATF

PFLD: A Practical Facial Landmark Detector

https://github.com/stdereka/knowledge-distillation

An Embarrassingly Simple Approach for Knowledge Distillation

https://github.com/stefkim/stylegan-batik

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/stergioc/smooth-transformer

Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance

https://github.com/stevehansen43/progressive_GAN

Progressive Growing of GANs for Improved Quality, Stability, and Variation

https://github.com/stevenpjg/RDPG

Memory-based control with recurrent neural networks

https://github.com/stevenpjg/ddpg-aigym

Continuous control with deep reinforcement learning

https://github.com/stevenzhou2017/darknet_AlexeyAB

Focal Loss for Dense Object Detection

https://github.com/stresearch/xfr

Explainable Face Recognition

https://github.com/strivebo/image_segmentation_dl

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/subinlab/model_based_rl_paper

Self-supervised Learning of Image Embedding for Continuous Control

https://github.com/sudhanshu2k/Signature-Verification

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/sudharavali/objectDetectionYOLO

Focal Loss for Dense Object Detection

https://github.com/suhas1999/Flip-kart-grid-challenge

Deep Residual Learning for Image Recognition

https://github.com/suhuijia/pggan

Progressive Growing of GANs for Improved Quality, Stability, and Variation

https://github.com/sukibean163/RetinaFace_Paddle

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/sulasen/race-events-recognition-1

Focal Loss for Dense Object Detection

https://github.com/sumathi16/FaceRecognition

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/sumitkutty/Anti-Spoof-Face-Recognition

Deep Residual Learning for Image Recognition

https://github.com/sumkumar/hiro_impl

Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

https://github.com/summerice9/AniGAN

AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation

https://github.com/sunfex/weighted-sac

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/sungwool/virtual_softmax

Virtual Class Enhanced Discriminative Embedding Learning

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