Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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보존된 평가 기록

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코드

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

https://github.com/kreimanlab/HumanIntentionInferenceZeroShot

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/krishnakarthi/COVID-19_Prediction

Deep Residual Learning for Image Recognition

https://github.com/ktjonsson/keras-ArcFace

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/ku2482/discor.pytorch

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

https://github.com/ku2482/gail-airl-ppo.pytorch

Proximal Policy Optimization Algorithms

https://github.com/ku2482/rljax

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

https://github.com/ku2482/rltorch

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

https://github.com/ku2482/slac.pytorch

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

https://github.com/ku2482/soft-actor-critic.pytorch

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

https://github.com/kuaikuaikim/DFace

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/kuangliu/pytorch-fpn

Focal Loss for Dense Object Detection

https://github.com/kukby/Mish-semantic-segmentation-pytorch

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/kukico/learning

DeepFaceLab: Integrated, flexible and extensible face-swapping framework

https://github.com/kumarnikhil936/Facial-Emotion-Recognition

Real-time Convolutional Neural Networks for Emotion and Gender Classification

https://github.com/kun-woo-park/Deeplearning_project_STL_10

Deep Residual Learning for Image Recognition

https://github.com/kushagra06/DDPG

Continuous control with deep reinforcement learning

https://github.com/kushagra06/SAC

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

https://github.com/kvijaykrish/Bosch_AI_VehicleClassification

Focal Loss for Dense Object Detection

https://github.com/kwantommy/fgvc6-kaggle-cassava-classification

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/kwotsin/TensorFlow-ENet

Deep Residual Learning for Image Recognition

https://github.com/kyle-dorman/bayesian-neural-network-blogpost

Deep Residual Learning for Image Recognition

https://github.com/kynkaat/improved-precision-and-recall-metric

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/kyranstar/Narcissus

Greedy Search for Descriptive Spatial Face Features

https://github.com/kyunghwan/darknet_v3

Focal Loss for Dense Object Detection

https://github.com/kzmttr/UMDFA

https://github.com/lab-midas/med_segmentation

Deep Residual Learning for Image Recognition

https://github.com/labmlai/annotated_deep_learning_paper_implementations

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/lanqingli1993/focal-iclr

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

https://github.com/laohur/LearnToCompareText

A Structured Self-attentive Sentence Embedding

https://github.com/laohur/RelationNet

A Structured Self-attentive Sentence Embedding

https://github.com/laoreja/face-identity-transformer

Learning Face Representation from Scratch

https://github.com/laoshiwei/face-anti-spoofing

Exploiting temporal and depth information for multi-frame face anti-spoofing

https://github.com/laowang666888/HRNET

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/larsoncs/face_detect

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/lava-lab/safe-slac

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

https://github.com/layumi/Person_reID_baseline_pytorch

Circle Loss: A Unified Perspective of Pair Similarity Optimization

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