Skip to search formSkip to main contentSkip to account menu

Caltech 101

Caltech 101 is a data set of digital images created in September 2003 and compiled by Fei-Fei Li, Marco Andreetto, Marc 'Aurelio Ranzato and Pietro… 
Wikipedia (opens in a new tab)

Related topics

Related topics

11 relations
AliasingCompression artifactComputer visionFlickr

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2020
2020
A novel Pooling Block for improving lightweight deep neural networks
2019
2019
Simultaneous Classification and Novelty Detection Using Deep Neural Networks
Deep neural networks have achieved great success in classification tasks during the last years. However, one major problem to the… 
2015
2015
Detection guided deconvolutional network for hierarchical feature learning
2014
2014
Unsupervised Image Classification by Probabilistic Latent Semantic Analysis for the Annotation of Images
Image annotation has been identified to be a suitable means by which the semantic gap which has made the accuracy of Content… 
2014
2014
Adaptive spatial partition learning for image classification
2014
2014
Low-rank decomposition and Laplacian group sparse coding for image classification
2013
2013
A contour-based shape descriptor for biomedical image classification and retrieval
Contours, object blobs, and specific feature points are utilized to represent object shapes and extract shape descriptors that… 
Review
2013
Review
2013
A Novel Approach for Image Retrieval System Combining Color, Shape & Texture Features
In many areas of commerce, government, academia, and hospitals, large collections of digital images are being created. Many of… 
2013
2013
Learning Multiple Non-linear Sub-spaces Using K-RBMs
Understanding the nature of data is the key to building good representations. In domains such as natural images, the data comes… 
2009
2009
Bayesian Localized Multiple Kernel Learning
Multiple kernel learning approaches to multi-view learning [1, 11, 7] have recently become very popular since they can easily… 
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)