https://github.com/NiWaRe/DPBenchmark
공식 코드SmoothNets: Optimizing CNN architecture design for differentially private deep learning
Papers with Code 아카이브
Image Classification with Differential Privacy
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Image Classification with Differential Privacy is an improved version of the image classification task whereby the final classification output only describe the patterns of groups within the dataset while withholding information about individuals in the dataset.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/TUM-AIMED/2.5DAttention
공식 코드Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
https://github.com/deepmind/jax_privacy
공식 코드Unlocking High-Accuracy Differentially Private Image Classification through Scale
https://github.com/facebookresearch/tan
공식 코드https://github.com/google-research/dp-imagenet
공식 코드https://github.com/hlzl/equivariant
공식 코드Equivariant Differentially Private Deep Learning: Why DP-SGD Needs Sparser Models
https://github.com/tayebiarasteh/dp_cxr
공식 코드Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
https://github.com/tayebiarasteh/privacydomain
공식 코드https://github.com/google-deepmind/jax_privacy
Unlocking High-Accuracy Differentially Private Image Classification through Scale
https://github.com/spalabucr/bb-audit-dpsgd
Unlocking High-Accuracy Differentially Private Image Classification through Scale
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.