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Datasets/Fashion-MNIST

Fashion-MNIST

ImagesMITIntroduced 2017-01-01

Fashion-MNIST is a dataset comprising of 28×28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST shares the same image size, data format and the structure of training and testing splits with the original MNIST.

Source: Generative Probabilistic Novelty Detection with Adversarial Autoencoders Image Source: https://github.com/zalandoresearch/fashion-mnist

Benchmarks

Anomaly Detection/ROC AUCAnomaly Detection/AUC (outlier ratio = 0.5)Anomaly Detection/AUC-ROCClustering Algorithms Evaluation/ARIClustering Algorithms Evaluation/F1-scoreClustering Algorithms Evaluation/NMIGeneral Classification/AccuracyImage Classification/Percentage errorImage Classification/AccuracyImage Classification/Trainable ParametersImage Classification/NMIImage Classification/Power consumptionImage Clustering/AccuracyImage Clustering/NMIImage Generation/FIDImage Generation/PrecisionImage Generation/RecallOut-of-Distribution Detection/AUROCOutlier Detection/AUROCUnsupervised Anomaly Detection/AUC (outlier ratio = 0.5)Unsupervised Anomaly Detection/AUC-ROCUnsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly/AUC-ROCUnsupervised Anomaly Detection with Specified Settings -- 1% anomaly/AUC-ROCUnsupervised Anomaly Detection with Specified Settings -- 10% anomaly/AUC-ROCUnsupervised Anomaly Detection with Specified Settings -- 20% anomaly/AUC-ROCUnsupervised Anomaly Detection with Specified Settings -- 30% anomaly/AUC-ROC

Statistics

Papers
3,202
Benchmarks
26

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Tasks

Anomaly DetectionClustering Algorithms EvaluationDomain GeneralizationGeneral ClassificationImage ClassificationImage ClusteringImage GenerationModel PoisoningMultiview ClusteringOut-of-Distribution DetectionOutlier DetectionUnsupervised Anomaly DetectionUnsupervised Anomaly Detection with Specified Settings -- 0.1% anomalyUnsupervised Anomaly Detection with Specified Settings -- 1% anomalyUnsupervised Anomaly Detection with Specified Settings -- 10% anomalyUnsupervised Anomaly Detection with Specified Settings -- 20% anomalyUnsupervised Anomaly Detection with Specified Settings -- 30% anomaly