Small Data Image Classification
57 papers with code • 12 benchmarks • 9 datasets
Supervised image classification with tens to hundreds of labeled training examples.
Benchmarks
These leaderboards are used to track progress in Small Data Image Classification
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Datasets
CIFAR-10
CIFAR-100
CUB-200-2011
UCF-Crime
TMED
Ecoli
DEIC Benchmark
WikiChurches
Latest papers
Image Classification With Small Datasets: Overview and Benchmark
lorenzobrigato/gem
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IEEE Access 2022
However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common benchmark to allow for objective comparisons between published methods.
ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing
In this work, we address the problem of learning deep neural networks on small datasets.
TorchXRayVision: 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.
Tune It or Don't Use It: Benchmarking Data-Efficient Image Classification
cvjena/deic
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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.
S2D2Net: 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.
Parametric Scattering Networks
bentherien/ParametricScatteringNetworks
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CVPR 2022
The wavelet scattering transform creates geometric invariants and deformation stability.
About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data Annotations
DmitriiShubin/Variance-Aware-Training
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Self-supervised learning methods for computer vision have demonstrated the effectiveness of pre-training feature representations, resulting in well-generalizing Deep Neural Networks, even if the annotated data are limited.
Auxiliary Learning by Implicit Differentiation
AvivNavon/AuxiLearn
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ICLR 2021
Two main challenges arise in this multi-task learning setting: (i) designing useful auxiliary tasks; and (ii) combining auxiliary tasks into a single coherent loss.
Ensemble long short-term memory (EnLSTM) network
In this study, we propose an ensemble long short-term memory (EnLSTM) network, which can be trained on a small dataset and process sequential data.
Meta-Meta Classification for One-Shot Learning
arjish/meta-meta-classification
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We present a new approach, called meta-meta classification, to learning in small-data settings.
