Papers with Code 아카이브

3D Face Modelling

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코드

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

https://github.com/godofpdog/MobileFaceNet_keras

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/godwinrayanc/YOLOv1-Pytorch

Deep Residual Learning for Image Recognition

https://github.com/goingqs/pyramidbox

PyramidBox: A Context-assisted Single Shot Face Detector

https://github.com/gokulprasadthekkel/pytorch-multi-class-focal-loss

Focal Loss for Dense Object Detection

https://github.com/goncharom/PPOv1

Proximal Policy Optimization Algorithms

https://github.com/google-deepmind/csil

IQ-Learn: Inverse soft-Q Learning for Imitation

https://github.com/google-deepmind/spriteworld

COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration

https://github.com/google-research/dreamer

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/google/brain-tokyo-workshop/tree/master/WANNRelease

Weight Agnostic Neural Networks

https://github.com/google/evojax

Simple random search provides a competitive approach to reinforcement learning

https://github.com/google/flax/tree/master/examples/imagenet

Deep Residual Learning for Image Recognition

https://github.com/google/trax

Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

https://github.com/google/youtube-8m

YouTube-8M: A Large-Scale Video Classification Benchmark

https://github.com/gopi-kampli/ImageClusteringDL

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/gouthamvgk/facemesh_coreml_tf

LFFD: A Light and Fast Face Detector for Edge Devices

https://github.com/gowriaddepalli/ML_DL_Research_collab_base

Deep Reinforcement Learning framework for Autonomous Driving

https://github.com/gox-ai/hrnet-pose-api

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/gpleiss/limits_of_large_width

Deep Residual Learning for Image Recognition

https://github.com/gpspelle/Crowd-Counting

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/greatsharma/DeepLearning-Papers-Implementation

Deep Residual Learning for Image Recognition

https://github.com/gregoiredervaux/face_recognition

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/grey-eye/talking-heads

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

https://github.com/griffinbran/machines_best_friend

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/grip-unina/GANimageDetection

AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces

https://github.com/gscdit/Mood-Analysis

Real-time Convolutional Neural Networks for Emotion and Gender Classification

https://github.com/gstoica27/cpg_ppo

Proximal Policy Optimization Algorithms

https://github.com/gtrll/dartenv

Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

https://github.com/guillaumeboniface/reacher

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/gundeep15/SCD_AI_Project

Deep Residual Learning for Image Recognition

https://github.com/gunjan99/Review-of-World-Models-using-Latex

World Models

https://github.com/guofeng201507/ISA-IPA-2020-05-05-IS1PT-GRP-High5-StockTrading

Proximal Policy Optimization Algorithms

https://github.com/guoqiangqi/PFLD

PFLD: A Practical Facial Landmark Detector

https://github.com/guppykang/joshFaceV2

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks

https://github.com/gurkirt/realtime-action-detection

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/gurushantj/MTCNN

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks

https://github.com/gwbcho/dpo-replication

Distributional Policy Optimization: An Alternative Approach for Continuous Control

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