Small Data Image Classification
58 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Datasets
CIFAR-10
CIFAR-100
CUB-200-2011
UCF-Crime
TMED
Ecoli
DEIC Benchmark
WikiChurches
Most implemented papers
Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
ieee8023/covid-chestxray-dataset
•
•
The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community.
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection
pfjaeger/medicaldetectiontoolkit
•
•
The proposed architecture recaptures discarded supervision signals by complementing object detection with an auxiliary task in the form of semantic segmentation without introducing the additional complexity of previously proposed two-stage detectors.
Learning What and Where to Transfer
alinlab/L2T-ww
•
•
To address the issue, we propose a novel transfer learning approach based on meta-learning that can automatically learn what knowledge to transfer from the source network to where in the target network.
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
On VOC07 testbed for few-shot image classification tasks on ImageNet with transfer learning (Goyal et al., 2019), replacing the linear SVM currently used with a Convolutional NTK SVM consistently improves performance.
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Convolutional neural networks (CNNs) work well on large datasets.
CuMF_SGD: Fast and Scalable Matrix Factorization
overcomes the issue of memory discontinuity.
Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimers disease
Lastly, with proper FR and FS, the performance of diffusion MRI features is comparable to that of T1w MRI.
DeepMoD: Deep learning for Model Discovery in noisy data
PhIMaL/DeePyMoD
•
•
We introduce DeepMoD, a Deep learning based Model Discovery algorithm.
Rigid-Soft Interactive Learning for Robust Grasping
bionicdl-sustech/DeepClawBenchmark
•
•
We use soft, stuffed toys for training, instead of everyday objects, to reduce the integration complexity and computational burden and exploit such rigid-soft interaction by changing the gripper fingers to the soft ones when dealing with rigid, daily-life items such as the Yale-CMU-Berkeley (YCB) objects.
On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location
oskyhn/CNNs-Without-Borders
•
•
CVPR 2020
In this paper we challenge the common assumption that convolutional layers in modern CNNs are translation invariant.
