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
Most implemented papers
High-risk learning: acquiring new word vectors from tiny data
Distributional semantics models are known to struggle with small data.
Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data
In this paper, we demonstrate that unsupervised learning algorithms perform competitively with supervised algorithms for detecting MAs on EDA data collected in both a lab-based setting and a real-world setting comprising about 23 hours of data.
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
While there is currently a lot of enthusiasm about "big data", useful data is usually "small" and expensive to acquire.
OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning
jlezama/OrthogonalLowrankEmbedding
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Deep neural networks trained using a softmax layer at the top and the cross-entropy loss are ubiquitous tools for image classification.
Deep Learning Approach for Very Similar Objects Recognition Application on Chihuahua and Muffin Problem
We propose the deep transfer learning method which could be tackled all this type of problems not limited to just Chihuahua or muffin problem.
Capturing Structure Implicitly from Time-Series having Limited Data
Most of the current literature in these fields involve visualizing the time-series for noticeable structure and hard coding them into pre-specified parametric functions.
Learning to Promote Saliency Detectors
The categories and appearance of salient objects vary from image to image, therefore, saliency detection is an image-specific task.
OLÃ: Orthogonal Low-Rank Embedding - A Plug and Play Geometric Loss for Deep Learning
jlezama/OrthogonalLowrankEmbedding
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CVPR 2018
Deep neural networks trained using a softmax layer at the top and the cross-entropy loss are ubiquitous tools for image classification.
A Semi-Supervised Data Augmentation Approach using 3D Graphical Engines
To evaluate the performance of our synthesized datasets in training deep learning-based models, we generated a large synthetic human pose dataset, called ScanAva using 3D scans of only 7 individuals based on our proposed augmentation approach.
The Fast and the Flexible: training neural networks to learn to follow instructions from small data
rezkaaufar/fast-and-flexible
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WS 2019
Learning to follow human instructions is a long-pursued goal in artificial intelligence.
