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Lightweight Face Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Lightweight Face Recognition models are a group of face recognition models with lightweight backbones, which can be used for mobile or edge device applications.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data
EFaR 2023: Efficient Face Recognition Competition
EdgeFace: Efficient Face Recognition Model for Edge Devices
GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations
QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantization
PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation
MixFaceNets: Extremely Efficient Face Recognition Networks
SqueezeFacePoseNet: Lightweight Face Verification Across Different Poses for Mobile Platforms
VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition
Towards Flops-constrained Face Recognition
SeesawFaceNets: sparse and robust face verification model for mobile platform
ShrinkTeaNet: Million-scale Lightweight Face Recognition via Shrinking Teacher-Student Networks
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.