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/kainoj/keep-pulse

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

https://github.com/kairproject/kair_algorithms_draft

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

https://github.com/kaist-dmlab/SELFIE

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/kaixindelele/DRLib

Overcoming Exploration in Reinforcement Learning with Demonstrations

https://github.com/kaiyangzhou/on-device-dg

Learning to Prompt for Vision-Language Models

https://github.com/kakaoenterprise/BroadFace

BroadFace: Looking at Tens of Thousands of People at Once for Face Recognition

https://github.com/kalaivani-s/self-attentive-sent-emb

A Structured Self-attentive Sentence Embedding

https://github.com/kalelpark/ral

Focal Loss for Dense Object Detection

https://github.com/kalifou/robotics-rl-srl

Simple random search provides a competitive approach to reinforcement learning

https://github.com/kamanphoebe/motiondetection

Deep Residual Learning for Image Recognition

https://github.com/karan96/NewOne

Focal Loss for Dense Object Detection

https://github.com/karaposu/facexformer-pipeline

FaceXFormer: A Unified Transformer for Facial Analysis

https://github.com/karim-abdel/fact

Explaining RL Decisions with Trajectories

https://github.com/karlchahine/neural-cover-selection-for-image-steganography

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

https://github.com/kartik-3004/facexbench

SegFace: Face Segmentation of Long-Tail Classes

https://github.com/kaseris/ILSVRCPlus

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/kaushal-k/Deep-Emotion

Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network

https://github.com/kaushalshetty/Structured-Self-Attention

A Structured Self-attentive Sentence Embedding

https://github.com/kavosh8/MAC

Mean Actor Critic

https://github.com/kawshik8/DL-project

Focal Loss for Dense Object Detection

https://github.com/kayuksel/pytorch-ars

Simple random search provides a competitive approach to reinforcement learning

https://github.com/kbpi314/iclust_paper

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/kbsriharsha/FacialEmotionAnalysis

Real-time Convolutional Neural Networks for Emotion and Gender Classification

https://github.com/kc-ml2/SimpleDreamer

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/kclip/localized-adaptive-risk-control

Deep Residual Learning for Image Recognition

https://github.com/kdally/fault-tolerant-flight-control-drl

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

https://github.com/kdhingra307/temp

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/keetsky/Net_ghostVLAD-pytorch

GhostVLAD for set-based face recognition

https://github.com/keillernogueira/exploit-cnn-rs

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/ken-power/CVND-FacialKeypointDetection

Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet

https://github.com/kenkenling/NLP

A Structured Self-attentive Sentence Embedding

https://github.com/kenomo/industrial-clip

Learning to Prompt for Vision-Language Models

https://github.com/kentaroy47/BlazeFace_Person.pytorch

BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs

https://github.com/keras-team/keras-applications

Deep Residual Learning for Image Recognition

https://github.com/khai9xht/Multitask_head_pose

Simultaneous face detection and 360 degree headpose estimation

https://github.com/khaled2ahmed/k2a

Focal Loss for Dense Object Detection

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