Small Data Image Classification
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Benchmarks
DEIC Benchmark
CIFAR-10, 500 Labels
CIFAR-10, 100 Labels
CIFAR-10, 1000 Labels
CIFAR-100, 1000 Labels
cifar10, 10 labels
CIFAR-10, 250 Labels
Most implemented
Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
Learning What and Where to Transfer
Papers
HydraMix: Multi-Image Feature Mixing for Small Data Image Classification
Training deep neural networks requires datasets with a large number of annotated examples. The collection and annotation of these datasets is not only extremely expensive but also faces legal and privacy problems. These …
Data Augmentationimage-classificationImage ClassificationSmall Data Image ClassificationImage Classification With Small Datasets: Overview and Benchmark
Image classification with small datasets has been an active research area in the recent past. However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthf…
Classificationimage-classificationImage ClassificationSmall Data Image ClassificationChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing
Deep convolutional neural networks require large amounts of labeled data samples. For many real-world applications, this is a major limitation which is commonly treated by augmentation methods. In this work, we address t…
ClassificationData Augmentationimage-classificationImage Classification+1TorchXRayVision: A library of chest X-ray datasets and models
TorchXRayVision is an open source software library for working with chest X-ray datasets and deep learning models. It provides a common interface and common pre-processing chain for a wide set of publicly available chest…
Image ClassificationMedical Image RetrievalMedical Image SegmentationMedical X-Ray Image Segmentation+3Tune It or Don't Use It: Benchmarking Data-Efficient Image Classification
Data-efficient image classification using deep neural networks in settings, where only small amounts of labeled data are available, has been an active research area in the recent past. However, an objective comparison be…
Benchmarkingimage-classificationImage ClassificationSmall Data Image ClassificationS2D2Net: An Improved Approach For Robust Steel Surface Defects Diagnosis With Small Sample Learning
Surface defect recognition of products is a necessary process to guarantee the quality of industrial production. This paper proposes a hybrid model, S2D2Net (Steel Surface Defect Diagnosis Network), for an efficient and …
Defect DetectionDiversityRobust classificationSmall Data Image Classification+1