- Selecting Receptive Fields in Deep Networks (NIPS 2011)
Cited 0 times. 82% - The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization (ICML 2011)
Cited 1 time. 81.5% - High-Performance Neural Networks for Visual Object Classification (2011)
Cited 3 times. 80.49% - Object Recognition with Hierarchical Kernel Descriptors (CVPR 2011)
Cited 2 times. 80% - An Analysis of Single-Layer Networks in Unsupervised Feature Learning (NIPS Workshop 2010)
Cited 18 times. 79.6%
Additional info: K-means (Triangle, 4000 features)
Homepage: Link - Convolutional Deep Belief Networks on CIFAR-10 (2010)
Cited 7 times. 78.9%
Additional info: 2 layers - Semiparametric Latent Variable Models for Guided Representation (2011)
Cited 0 times. 77.9% - Kernel Descriptors for Visual Recognition (NIPS 2010)
Cited 6 times. 76%
Additional info: KDES-A - Image Descriptor Learning Using Deep Networks (2010)
Cited 0 times. 75.18% - Improved Local Coordinate Coding using Local Tangents (ICML 2010)
Cited 8 times. 74.5%
Additional info: Linear SVM with improved LCC - Tiled convolutional neural networks (NIPS 2010)
Cited 4 times. 73.1%
Additional info: Deep Tiled CNNs (s=4, with finetuning) - Semiparametric Latent Variable Models for Guided Representation (2011)
Cited 0 times. 72.28%
Additional info: Alpha = 0.01 - Modelling Pixel Means and Covariances Using Factorized Third-Order Boltzmann Machines (CVPR 2010)
Cited 14 times. 71%
Additional info: mcRBM-DBN (11025-8192-8192), 3 layers, PCA’d images - On Autoencoders and Score Matching for Energy Based Models (ICML 2011)
Cited 0 times. 65.5% - Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images (JMLR 2010)
Cited 14 times. 65.3%
Additional info: 4,096 3-Way, 3 layer, ZCA’d images - Learning invariant features through local space contraction (2011)
Cited 0 times. 52.14%
Sunday, February 27, 2011
Table of results for CIFAR-10 dataset
This is a table documenting some of the best results some paper obtained in CIFAR-10 dataset.
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3 comments:
This is really great! I've been looking for something like this, and as far as I can see it is pretty complete.
The recently published "Selecting receptive fields in deep networks" http://www.stanford.edu/~acoates
reaches 82% on this dataset.
@rodrigob: Thank you so much for the information. The blog is updated now :)
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