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/ehijano/Faster-RCNN-Global-Wheat-Detection

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/eladrich/pix2vertex.pytorch

Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation

https://github.com/eladsar/rbi

Proximal Policy Optimization Algorithms

https://github.com/elangarka/Regularized-Xception-FER-Extra-Training-Data-Step-Decay-Learning-Rate

Challenges in Representation Learning: A report on three machine learning contests

https://github.com/elhamod/phylonn

Latent Space Factorisation and Manipulation via Matrix Subspace Projection

https://github.com/elliott-iadvize/ocr-aws-distributed

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/elliottzheng/face-detection

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/eltechno/pulse

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

https://github.com/embracesource-cv-com/FCHD

FCHD: Fast and accurate head detection in crowded scenes

https://github.com/emerge-lab/nocturne_lab

Proximal Policy Optimization Algorithms

https://github.com/emilianavt/OpenSeeFace

Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression

https://github.com/enikhu/dreamer

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/enpasos/muzero

Online and Offline Reinforcement Learning by Planning with a Learned Model

https://github.com/enzomuschik/distilfnd

Deep Residual Learning for Image Recognition

https://github.com/epoc88/PFLD_68pts_Pytorch_2020

PFLD: A Practical Facial Landmark Detector

https://github.com/eric-erki/ID-Card-Segmentation

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

https://github.com/eric-erki/IdenProf

Deep Residual Learning for Image Recognition

https://github.com/eric-erki/Visualising-Facial-Expression-Changes

Web-based visualisation of head pose and facial expressions changes: monitoring human activity using depth data

https://github.com/eric-erki/darknet

Focal Loss for Dense Object Detection

https://github.com/eric-erki/insightface

MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition

https://github.com/eric-erki/robotics-rl-srl

Simple random search provides a competitive approach to reinforcement learning

https://github.com/eric-erki/yolov3

Focal Loss for Dense Object Detection

https://github.com/eric-mitchell/mend

Editable Neural Networks

https://github.com/eric-mitchell/serac

Editable Neural Networks

https://github.com/eriklindernoren/PyTorch-GAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/ermongroup/higher_order_invariance

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

https://github.com/ermuur/darknet

Focal Loss for Dense Object Detection

https://github.com/esadsetts/Mod_4_project

A Structured Self-attentive Sentence Embedding

https://github.com/eshaanagarwal/hr-net-implementation

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/esimov/pigo

Object Detection with Pixel Intensity Comparisons Organized in Decision Trees

https://github.com/espectre/GCNs_on_imbalanced_datasets

Learning to Cluster Faces on an Affinity Graph

https://github.com/espressif/esp-who

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

https://github.com/eth-sri/llm-anonymization

A Synthetic Dataset for Personal Attribute Inference

https://github.com/eth212/Qmind_ModelNet10_PointNet

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

https://github.com/ethanluoyc/compile-jax

CompILE: Compositional Imitation Learning and Execution

https://github.com/evieq01/oodil

Proximal Policy Optimization Algorithms

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