Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

2

평가표

2

보존된 평가 기록

3

코드

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

https://github.com/unstablecursor/robotino_rl

Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments

https://github.com/uoe-agents/derl

#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

https://github.com/usnistgov/image-classification-resnet50

Deep Residual Learning for Image Recognition

https://github.com/usnistgov/image-regression-resnet50

Deep Residual Learning for Image Recognition

https://github.com/ut-vision/rot-mvgaze

Learning-by-Novel-View-Synthesis for Full-Face Appearance-Based 3D Gaze Estimation

https://github.com/utsawk/CarND-Traffic-Sign-Classifier-Project

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/uvipen/super-mario-bros-ppo-pytorch

Proximal Policy Optimization Algorithms

https://github.com/uw-hai/limeade

Deep Residual Learning for Image Recognition

https://github.com/v-trungdt21/MaskTheFace

Masked Face Recognition for Secure Authentication

https://github.com/vaibhavsaxena11/cwvae

Learning Latent Dynamics for Planning from Pixels

https://github.com/valentingol/GANJax

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

https://github.com/vantupham/darknet

Focal Loss for Dense Object Detection

https://github.com/varennes/deep-autoencoder

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/varshaneya/Res-SE-Net

Deep Residual Learning for Image Recognition

https://github.com/varuncs2011/rl

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

https://github.com/varununleashed/tiny_faces_temp

Can we still avoid automatic face detection?

https://github.com/vayuvegula/kerasModels

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/vbonifaci/l1opt

Fast L1-Minimization Algorithms For Robust Face Recognition

https://github.com/vcadillog/PPO-Mario-Bros-Tensorflow-2

Proximal Policy Optimization Algorithms

https://github.com/vendyv/CodeSpeedy_Internship

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/vermashresth/damage-aware-PPO

Proximal Policy Optimization Algorithms

https://github.com/vgangal101/VGG_models

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/vgangal101/resnet_models

Deep Residual Learning for Image Recognition

https://github.com/vgudapati/DRLND_Collaboration_Competetion

Distributed Distributional Deterministic Policy Gradients

https://github.com/vheuthe/microbot_rl

Proximal Policy Optimization Algorithms

https://github.com/vicksam/fer-model

Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution

https://github.com/victespinoza/NeuralStyleTransfer

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/viebboy/MultilinearCompressiveLearningWithPrior

Multilinear Compressive Learning

https://github.com/vietanhdev/open-adas

Deep Residual Learning for Image Recognition

https://github.com/vietdhoang/resnet-18

Deep Residual Learning for Image Recognition

https://github.com/viethungluu/OCRetina

Focal Loss for Dense Object Detection

https://github.com/vijishmadhavan/SkinDeep

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

https://github.com/vikramkarthikeyan/Face-R-FCN

Detecting Faces Using Region-based Fully Convolutional Networks

https://github.com/vill-lab/2024-aaai-hpt

Learning to Prompt for Vision-Language Models

https://github.com/vinairesearch/lemul

Learning Face Representation from Scratch

https://github.com/vinay-EIP/EIP-session-5-assignment

Deep Residual Learning for Image Recognition

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