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 with no code
On Coresets for Support Vector Machines
A coreset is a small, representative subset of the original data points such that a models trained on the coreset are provably competitive with those trained on the original data set.
On a scalable entropic breaching of the overfitting barrier in machine learning
Overfitting and treatment of "small data" are among the most challenging problems in the machine learning (ML), when a relatively small data statistics size $T$ is not enough to provide a robust ML fit for a relatively large data feature dimension $D$.
Better Multi-class Probability Estimates for Small Data Sets
Many classification applications require accurate probability estimates in addition to good class separation but often classifiers are designed focusing only on the latter.
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
The review also includes a general overview of the different ML techniques, but as most applications in porous materials use supervised ML our review is focused on the different approaches for supervised ML.
Identifying Table Structure in Documents using Conditional Generative Adversarial Networks
In many industries, as well as in academic research, information is primarily transmitted in the form of unstructured documents (this article, for example).
MRI Pulse Sequence Integration for Deep-Learning Based Brain Metastasis Segmentation
We illustrate not only the generalizability of the network but also the utility of this robustness when applying the trained model to data from a different center, which does not use the same pulse sequences.
Generalizable prediction of academic performance from short texts on social media
We build a model to predict academic performance from users' posts on VK and then apply it to a different context.
Learning to Control Latent Representations for Few-Shot Learning of Named Entities
Our proposed solution is straightforward and simple: we train a controller to execute an optimal sequence of reading and writing operations on an external memory with the goal of leveraging diverse activations from the past and provide accurate predictions.
CD2 : Combined Distances of Contrast Distributions for the Assessment of Perceptual Quality of Image Processing
The quality of visual input is very important for both human and machine perception.
Transfer Learning of fMRI Dynamics
In this paper, we demonstrate a self-supervised pre-training method that enables us to pre-train directly on fMRI dynamics of healthy control subjects and transfer the learning to much smaller datasets of schizophrenia.
