https://github.com/j-nowak/covid-19
Very Deep Convolutional Networks for Large-Scale Image Recognition
Papers with Code 아카이브
3D Face Modelling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/j-void/ISL_v2v
https://github.com/j3soon/arxiv-utils
https://github.com/jachiam/cpo
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/jackaduma/nude-detect
https://github.com/jacke121/A-Light-and-Fast-Face-Detector-for-Edge-Devices
https://github.com/jacobgil/pytorch-grad-cam
https://github.com/jadevaibhav/Signature-verification-using-deep-learning
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/jadore801120/attention-is-all-you-need-pytorch
https://github.com/jaem-seo/AI_tokamak_control
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/jainpankul/Augmented-Random-Search
Simple random search provides a competitive approach to reinforcement learning
https://github.com/jakegrigsby/deep_control/blob/master/deep_control/ddpg.py
https://github.com/jakegrigsby/deep_control/blob/master/deep_control/sac.py
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/jakegrigsby/deep_control/blob/master/deep_control/td3.py
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/james77777778/keras-image-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jameswang287/Car-Detection
https://github.com/janZub-AI/EmotionDetectionVGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jangirrishabh/Overcoming-exploration-from-demos
Overcoming Exploration in Reinforcement Learning with Demonstrations
https://github.com/jankolf/ser-fiq-pytorch
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
https://github.com/jannine92/supervised_emotion_recognition
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/jaredaevans/FishGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/jason90330/EdgeFinal
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/jastfkjg/DDPG_Torcs_PyTorch
Simulated Car Racing Championship: Competition Software Manual
https://github.com/jattardo1/pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/javier-hernandezo/faceqgen
FaceQnet: Quality Assessment for Face Recognition based on Deep Learning
https://github.com/javier-hernandezo/faceqnet
FaceQnet: Quality Assessment for Face Recognition based on Deep Learning
https://github.com/jaydeepthik/kaggle-facial-expression-recognition
Convolutional Neural Networks for Facial Expression Recognition
https://github.com/jchaykow/AGN-pytorch
A General Framework for Adversarial Examples with Objectives
https://github.com/jcwleo/curiosity-driven-exploration-pytorch
https://github.com/jcwleo/random-network-distillation-pytorch
https://github.com/jday96314/Kuzushiji
https://github.com/jehovahxu/ca-gan
Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs
https://github.com/jemaw/gym-safety
Lyapunov-based Safe Policy Optimization for Continuous Control
https://github.com/jeromerony/Progressive_Growing_of_GANs-PyTorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/jesbu1/carl
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
https://github.com/jesperhauch/polynomial_deep_learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.