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Small Data Image Classification

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

Papers

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Showing 126–150 of 179 papers

TitleStatusHype
Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification—0
Large-Scale Cox Process Inference using Variational Fourier Features—0
Meta Reinforcement Learning with Latent Variable Gaussian Processes—0
Capturing Structure Implicitly from Time-Series having Limited DataCode0
Predicting Oral Disintegrating Tablet Formulations by Neural Network Techniques—0
Local Contrast Learning—0
Lie Transform--based Neural Networks for Dynamics Simulation and Learning—0
VIBNN: Hardware Acceleration of Bayesian Neural Networks—0
Deep Learning Approach for Very Similar Objects Recognition Application on Chihuahua and Muffin ProblemCode0
Representation Learning with Autoencoders for Electronic Health Records: A Comparative Study—0
OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep LearningCode0
One-Shot Coresets: The Case of k-Clustering—0
LDMNet: Low Dimensional Manifold Regularized Neural Networks—0
Capturing Localized Image Artifacts through a CNN-based Hyper-image Representation—0
Quality-Efficiency Trade-offs in Machine Learning for Text Processing—0
Ensemble and Reranking: Using Multiple Models in the NICT-2 Neural Machine Translation System at WAT2017—0
Improving Landmark Localization with Semi-Supervised Learning—0
`Fighting' or `Conflict'? An Approach to Revealing Concepts of Terms in Political Discourse—0
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential EquationsCode0
Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity DataCode0
Representation Learning on Large and Small Data—0
High-risk learning: acquiring new word vectors from tiny dataCode0
Learning like humans with Deep Symbolic Networks—0
A simple efficient density estimator that enables fast systematic search—0
Synthesizing Deep Neural Network Architectures using Biological Synaptic Strength Distributions—0
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