Papers with Code 아카이브

3D Face Modelling

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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/rowerliu/add

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

https://github.com/roysai/Humpback-whale-identity-challenge

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/roythuly/obac

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

https://github.com/rperezdattari/Interactive-Learning-with-Corrective-Feedback-for-Policies-based-on-Deep-Neural-Networks

Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks

https://github.com/rshnn/battleship

Proximal Policy Optimization Algorithms

https://github.com/rtanno21609/AdaptiveNeuralTrees

Deep Residual Learning for Image Recognition

https://github.com/ruidaniel/rbaca

Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics

https://github.com/ruinmessi/RFBNet

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/run-youngjoo/SC-FEGAN

SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color

https://github.com/runamu/compositional-conservatism

MOPO: Model-based Offline Policy Optimization

https://github.com/rupaai/60DaysOfUdacity

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/russellmendonca/maesn_suite

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/ry/tensorflow-resnet

Deep Residual Learning for Image Recognition

https://github.com/ryanwebster90/gen-overfitting-latent-recovery

Detecting Overfitting of Deep Generative Networks via Latent Recovery

https://github.com/ryubidragonfire/mydarknet

Focal Loss for Dense Object Detection

https://github.com/s-sd/task-amenability

Continuous control with deep reinforcement learning

https://github.com/s3nh/unet-midv500

MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream

https://github.com/saaries/PointNet

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

https://github.com/saber2011/darknet

Focal Loss for Dense Object Detection

https://github.com/saednasir/Helmet_Yolo

Focal Loss for Dense Object Detection

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

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

https://github.com/sagnik1511/Transfer-Learning-with-Python

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/sagpant/3dmm_cnn

Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network

https://github.com/sahilunagar/VGG16-model

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/saiamrit/Facenet-pytorch

Learning Face Representation from Scratch

https://github.com/saic-fi/bayesian-prompt-learning

Learning to Prompt for Vision-Language Models

https://github.com/saikrishnadas/ResNet-Pytorch

Deep Residual Learning for Image Recognition

https://github.com/sail-sg/offbench

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/saimj7/Action-Recognition-in-Real-Time

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/sainimohit23/FaceNet-Real-Time-face-recognition

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/saiteja-talluri/Speech2Face

Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks

https://github.com/sajjjadayobi/FaceLib

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/saloni1998/Cifar-10

Deep Residual Learning for Image Recognition

https://github.com/samiranrl/ODSC_2019_RL

Continuous control with deep reinforcement learning

https://github.com/samisoto/keras_cosine_based_loss

L2-constrained Softmax Loss for Discriminative Face Verification

https://github.com/sammyamajumdar/ResNet50

Deep Residual Learning for Image Recognition

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