Very deep convolutional neural network based image classification using small training sample size.
Shuying LiuWeihong DengPublished in: ACPR (2015)
Keyphrases
- sample size
- image classification
- number of training samples
- small sample
- sparse coding
- model selection
- random sample
- small sample size
- deep learning
- upper bound
- deep belief networks
- vc dimension
- random sampling
- covariance matrix
- statistical power
- statistical hypothesis testing
- progressive sampling
- feature extraction
- image representation
- pac learning
- worst case
- variance reduction
- small samples
- neural network
- generalization error
- training samples
- experimental design
- supervised learning
- unsupervised learning
- computational complexity
- hypothesis tests
- image features
- restricted boltzmann machine
- statistical tests
- sample complexity