Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

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보존된 평가 기록

3

코드

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

https://github.com/deepmodeling/deepmd-kit

Deep Residual Learning for Image Recognition

https://github.com/deepquestai/fire-smoke-dataset

Deep Residual Learning for Image Recognition

https://github.com/deftruth/torchlm

Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild

https://github.com/delmalih/MIAS-mammography-obj-detection

Focal Loss for Dense Object Detection

https://github.com/demul/ResidualNet

Deep Residual Learning for Image Recognition

https://github.com/dengpingfan/fsgan

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

https://github.com/denis19973/faceshifter_tornado

FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping

https://github.com/denizmguen/IANNWTF2019-Project

Continuous control with deep reinforcement learning

https://github.com/deontaepharr/Residual-Attention-Network

Deep Residual Learning for Image Recognition

https://github.com/derek-baker/group3

Spotting Macro- and Micro-expression Intervals in Long Video Sequences

https://github.com/detectionteamucas/retinanet_tensorflow

Focal Loss for Dense Object Detection

https://github.com/developer-smartwebzone/-reinforcement-learning-algorithms.

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

https://github.com/dfki-ric-underactuated-lab/torque_limited_simple_pendulum

Continuous control with deep reinforcement learning

https://github.com/dhaalves/CEAL_keras

Cost-Effective Active Learning for Deep Image Classification

https://github.com/dheerajsingh7351/facedetect

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

https://github.com/dhesenkamp/attentive-lstm

A Structured Self-attentive Sentence Embedding

https://github.com/dickreuter/neuron_poker

Proximal Policy Optimization Algorithms

https://github.com/didi/AoE

SeesawFaceNets: sparse and robust face verification model for mobile platform

https://github.com/discipleofhamilton/RetinaFace

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/dishen12/py03

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/divelab/vqa-text

Compact Bilinear Pooling

https://github.com/djcustard/GestureGAN

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

https://github.com/djwhsdj/vw-sdk

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/dkangin/baselines/tree/master/baselines/trpo_replay

On-Policy Trust Region Policy Optimisation with Replay Buffers

https://github.com/dl-maxwang/blazeface-tensorflow

BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs

https://github.com/dmiu-shell/deeprl-shell

Proximal Policy Optimization Algorithms

https://github.com/dmizr/phuber

Deep Residual Learning for Image Recognition

https://github.com/dmksjfl/smr

Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple Reuse

https://github.com/dmlc/gluon-nlp

A Structured Self-attentive Sentence Embedding

https://github.com/dmonterom/face_recognition_TF2

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/dmund95/Off-Policy-Deep-RL

Trajectory-Based Off-Policy Deep Reinforcement Learning

https://github.com/dnddnjs/pytorch-cifar10

Deep Residual Learning for Image Recognition

https://github.com/do4am/MultiviewFaceDetector

Multi-view Face Detection Using Deep Convolutional Neural Networks

https://github.com/doanmanhduy0210/ResearchPaperfacerecognitions

NormFace: L2 Hypersphere Embedding for Face Verification

https://github.com/doantienthongbku/ESRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/doantienthongbku/Implementation-patchnet

PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognition

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