Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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평가표

2

보존된 평가 기록

3

코드

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

https://github.com/Sangkwun/Resnet

Deep Residual Learning for Image Recognition

https://github.com/Sangohe/pgan_thesis

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

https://github.com/SanketAgrawal/Catagorical-Focal-Loss

Focal Loss for Dense Object Detection

https://github.com/Santara/RAIL

RAIL: Risk-Averse Imitation Learning

https://github.com/SaoYan/GenerativeSkinLesion

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

https://github.com/SaremS/WorldModel

World Models

https://github.com/SarthakGarg13/OTOMYCOSIS

Deep Residual Learning for Image Recognition

https://github.com/Sarthaks21/Facenet-with-MTCNN-and-SVM

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/Satyake/Triplet-Loss_Siamese_NN

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/Saurav0074/Cartoon-Face-Detection-and-Recognition

Bringing Cartoons to Life: Towards Improved Cartoon Face Detection and Recognition Systems

https://github.com/SeongwoongJo/Multimodal-Emotional-Recognition

Frame attention networks for facial expression recognition in videos

https://github.com/SeoroMin/transformer_pytorch

A Structured Self-attentive Sentence Embedding

https://github.com/SeoroMin/transformer_pytorch_ver2

A Structured Self-attentive Sentence Embedding

https://github.com/SergiPonsa/Reinforcement-Learning-Sergi

Continuous control with deep reinforcement learning

https://github.com/Seth-Park/MultimodalExplanations

Compact Bilinear Pooling

https://github.com/SethEBaldwin/Resnet

Deep Residual Learning for Image Recognition

https://github.com/SeungeonBaek/continuous-agents-test

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/SeungyounShin/Adaptive-Wing-Loss-for-Robust-Face-Alignment-via-Heatmap-Regression

Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression

https://github.com/SeungyounShin/EXTD

EXTD: Extremely Tiny Face Detector via Iterative Filter Reuse

https://github.com/SeungyounShin/GroupFace

GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition

https://github.com/SevenZhan/Pytorch

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/SforAiDl/KD_Lib

Knowledge Distillation via Route Constrained Optimization

https://github.com/ShadowShadowWong/update-pointnet-work

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

https://github.com/ShaharrHorn/Detection-Game-logic

Focal Loss for Dense Object Detection

https://github.com/ShaneFlandermeyer/bmpc-jax

Bootstrapped Model Predictive Control

https://github.com/ShaojieJiang/tldr

Focal Loss for Dense Object Detection

https://github.com/ShawK91/Evolutionary-Reinforcement-Learning

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/ShawnDing1994/AOFP

RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition

https://github.com/ShawnNew/shape-alpha-matting

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Sheldonmao/Learning-To-walk

Continuous control with deep reinforcement learning

https://github.com/ShenYujun/InterFaceGAN

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

https://github.com/ShiqiYu/libfacedetection

Detect Faces Efficiently: A Survey and Evaluations

https://github.com/Shivamt99/Capstone_Project

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Shraddha2013/customyolo

Focal Loss for Dense Object Detection

https://github.com/Shraddha2013/darknett

Focal Loss for Dense Object Detection

https://github.com/Shraddha2013/darknetyol

Focal Loss for Dense Object Detection

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