Papers with Code · CC BY-SA 4.0

Fashion-MNIST

아카이브 평가 34건 · 마지막 적재/변경 2026-10-30

평가 기록

과거 수집 기록이며 최신 순위가 아닙니다. 데이터셋 버전·split·실험 조건과 지표 방향이 검증되지 않은 결과는 직접 비교할 수 없습니다. 날짜는 논문 날짜이며 최신 평가일을 뜻하지 않습니다.

표가 화면보다 넓으면 좌우로 스크롤하여 논문과 구현 링크를 확인하세요.

원본 기록 순서 · 순위 미부여
모델	원본 지표·값	논문	구현
PreAct-ResNet18 + FMix	Percentage error 3.64	FMix: Enhancing Mixed Sample Data Augmentation 2020-02-27	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5
Random Erasing	Percentage error 3.65	Random Erasing Data Augmentation 2017-08-16	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 · 코드 9 · 코드 10 · 코드 11 · 코드 12 · 코드 13 · 코드 14 · 코드 15 · 코드 16 · 코드 17 · 코드 18
E2E-3M	Percentage error 4.08	Rethinking Recurrent Neural Networks and Other Improvements for Image Classification 2020-07-30	코드 1
VGG8B(2x) + LocalLearning + CO	Percentage error 4.14	Training Neural Networks with Local Error Signals 2019-01-20	코드 1 · 코드 2
Inception v3	Percentage error 5.56 Accuracy 94.44	CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters 2022-03-29	코드 1
WaveMixLite	Percentage error 5.68	WaveMix: A Resource-efficient Neural Network for Image Analysis 2022-05-28	코드 1
Local Mixup DenseNet	Percentage error 5.97	Preventing Manifold Intrusion with Locality: Local Mixup 2022-01-12	코드 1
TextCaps	Percentage error 6.29	TextCaps : Handwritten Character Recognition with Very Small Datasets 2019-04-17	코드 1 · 코드 2 · 코드 3
R-ExplaiNet-26	Percentage error 6.55 Accuracy 93.45 Trainable Parameters 892362	Learning local discrete features in explainable-by-design convolutional neural networks 2024-10-31	코드 1
ResNet-18 + Vision Eagle Attention	Percentage error 6.70 Accuracy 93.30	Vision Eagle Attention: a new lens for advancing image classification 2024-11-15	코드 1
StiDi-BP in R-CSNN	Percentage error 7.2 Accuracy 92.8	Spike time displacement based error backpropagation in convolutional spiking neural networks 2021-08-31	연결 없음
NeuPDE	Percentage error 7.6	NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data 2019-08-08	연결 없음
Star Algorithm on LeNet	Percentage error 7.7 Accuracy 92.3	Star algorithm for NN ensembling 2022-06-01	코드 1
ResNet-18	Percentage error 7.72 Accuracy 92.28	Vision Eagle Attention: a new lens for advancing image classification 2024-11-15	코드 1
SpeedyLiteVision Network	Percentage error 8.4 Accuracy 91.60 Trainable Parameters 35978	연결 없음 논문 날짜 미기재	연결 없음
CTM-8000 (Convolutional Tsetlin Machine)	Percentage error 8.5 Accuracy 91.5 Trainable Parameters 527250	The Convolutional Tsetlin Machine 2019-05-23	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 · 코드 9
Convolutional PMM (Parametric Matrix Model)	Percentage error 9.11 Accuracy 90.89 Trainable Parameters 278280	Parametric Matrix Models 2024-01-22	연결 없음
DENSER	Percentage error 9.6 Accuracy 0.904 Power consumption 97.8	Towards Physical Plausibility in Neuroevolution Systems 2024-01-31	코드 1
OTTT	Percentage error 9.6	Online Training Through Time for Spiking Neural Networks 2022-10-09	코드 1
ENERGIZE	Percentage error 9.8 Accuracy 0.902 Power consumption 71.92	Towards Physical Plausibility in Neuroevolution Systems 2024-01-31	코드 1
PMM (Parametric Matrix Model)	Percentage error 11.42 Accuracy 88.58 Trainable Parameters 16744	Parametric Matrix Models 2024-01-22	연결 없음
CTM-250 (Convolutional Tsetlin Machine)	Percentage error 11.75 Accuracy 88.25 Trainable Parameters 31750	The Convolutional Tsetlin Machine 2019-05-23	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 · 코드 9
GECCO	Percentage error 11.91 Accuracy 88.09	A Single Graph Convolution Is All You Need: Efficient Grayscale Image Classification 2024-02-01	코드 1
pFedBreD_ns_mg	Accuracy 99.06	Personalized Federated Learning with Hidden Information on Personalized Prior 2022-11-19	연결 없음
LR-Net	Accuracy 95.03	LR-Net: A Block-based Convolutional Neural Network for Low-Resolution Image Classification 2022-07-19	코드 1
Tsetlin Machine Composites	Accuracy 93.0	TMComposites: Plug-and-Play Collaboration Between Specialized Tsetlin Machines 2023-09-09	코드 1
CNN+ Wilson-Cowan model RNN	Accuracy 91.35	Learning in Wilson-Cowan model for metapopulation 2024-06-24	코드 1
FastSNN (CNN)	Accuracy 90.57	Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks 2022-05-30	코드 1
FastSNN (MLP)	Accuracy 89.05	Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks 2022-05-30	코드 1
Wilson-Cowan model RNN	Accuracy 88.39	Learning in Wilson-Cowan model for metapopulation 2024-06-24	코드 1