# Small Data Image Classification

57 papers with code • 12 benchmarks • 10 datasets

Supervised image classification with tens to hundreds of labeled training examples.

# Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data

11 Apr 2020

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.

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# Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection

21 Nov 2018

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.

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# Learning What and Where to Transfer

15 May 2019

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.

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# Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference

6 Jun 2015

Convolutional neural networks (CNNs) work well on large datasets.

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# 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.

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# CuMF_SGD: Fast and Scalable Matrix Factorization

19 Oct 2016

overcomes the issue of memory discontinuity.

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# Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimers disease

28 Dec 2018

Lastly, with proper FR and FS, the performance of diffusion MRI features is comparable to that of T1w MRI.

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# DeepMoD: Deep learning for Model Discovery in noisy data

20 Apr 2019

We introduce DeepMoD, a Deep learning based Model Discovery algorithm.

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# Rigid-Soft Interactive Learning for Robust Grasping

29 Feb 2020

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.

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# Stochastic Function Norm Regularization of Deep Networks

30 May 2016

In this paper, we study the feasibility of directly using the $L_2$ function norm for regularization.

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