https://github.com/niranjana98/Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
Papers with Code 아카이브
3D Face Modelling
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nitthilan/pommerman
https://github.com/niuxiaozhang/mxnet-retinaface-to-gluon
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/njordsir/Clustering-Faces
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/nmhaddad/python-machine-learning-youtube
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nmsquared/CS7641-Assignment-4
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/nodefluxio/automatic-masking-master
https://github.com/noelmat/facelandmarks_wingloss
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
https://github.com/nohboogy/gym
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/northeastsquare/effficientnet
https://github.com/notecola/facenet-light
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/nova26/facePoseEstimation
https://github.com/nric/ProximalPolicyOptimizationContinuousKeras
https://github.com/nsom/Basic-Models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nsom/VGG16
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ntrin/CIFAR_10_ML
https://github.com/nu11us/WC-MS-Celeb
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
https://github.com/nveldt/LamCC
https://github.com/nvlabs/gbrl_sb3
https://github.com/oarriaga/paz
https://github.com/oawiles/FAb-Net
Self-supervised learning of a facial attribute embedding from video
https://github.com/obj2vec/obj2vec
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/obster991/RL-model-based-planning-from-scratch
https://github.com/octothrope/ARS
Simple random search provides a competitive approach to reinforcement learning
https://github.com/oellop/facial_expression
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/oh-lab/UTE-Uncertainty-aware-Temporal-Extension-
https://github.com/okeer/nst-tele-bot
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/oktapodi/ps_resNet
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/olaviinha/NeuralImageSuperResolution
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/olegpolivin/antisleep
https://github.com/olgi9911/MMDG
Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing
https://github.com/ollenilsson19/MAP-Elites-GAPG
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/ollenilsson19/PGA-MAP-Elites
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/ollewelin/libtorch-GPU-CNN-test-MNIST-with-Batchnorm
https://github.com/omaarelsherif/Face-Detection-Using-Deep-Learning
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/omaarelsherif/Face-Detection-Using-MTCNN
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.