Enhanced Image Classification With a Fast-Learning Shallow Convolutional Neural Network
We present a neural network architecture and training method designed to enable very rapid training and low implementation complexity. Due to its training speed and very few tunable parameters, the method has strong potential for applications requiring frequent retraining or online training. The approach is characterized by (a) convolutional filters based on biologically inspired visual processing filters, (b) randomly-valued classifier-stage input weights, (c) use of least squares regression to train the classifier output weights in a single batch, and (d) linear classifier-stage output units. We demonstrate the efficacy of the method by applying it to image classification. Our results match existing state-of-the-art results on the MNIST (0.37% error) and NORB-small (2.2% error) image classification databases, but with very fast training times compared to standard deep network approaches. The network's performance on the Google Street View House Number (SVHN) (4% error) database is also competitive with state-of-the art methods.
Code (0)
등록된 구현이 없습니다.
Tasks
General Classificationimage-classificationImage ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Shallow and Deep Convolutional Networks for Saliency Prediction
The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles. This paper, however, addresses the problem with a completely data-driven approach by…
PredictionSaliency PredictionLearning rotation invariant convolutional filters for texture classification
We present a method for learning discriminative filters using a shallow Convolutional Neural Network (CNN). We encode rotation invariance directly in the model by tying the weights of groups of filters to several rotated…
ClassificationGeneral Classificationimage-classificationImage Classification+1Hierarchical Transfer Convolutional Neural Networks for Image Classification
In this paper, we address the issue of how to enhance the generalization performance of convolutional neural networks (CNN) in the early learning stage for image classification. This is motivated by real-time application…
ClassificationGeneral Classificationimage-classificationImage ClassificationBoundary-based Image Forgery Detection by Fast Shallow CNN
Image forgery detection is the task of detecting and localizing forged parts in tampered images. Previous works mostly focus on high resolution images using traces of resampling features, demosaicing features or sharpnes…
DemosaickingImage Forgery DetectionGAMA-IR: Global Additive Multidimensional Averaging for Fast Image Restoration
Deep learning-based methods have shown remarkable success for various image restoration tasks such as denoising and deblurring. The current state-of-the-art networks are relatively deep and utilize (variants of) self att…
DeblurringDenoisingGPUImage Restoration