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.
  1. Selecting Receptive Fields in Deep Networks (NIPS 2011)
    Cited 0 times. 82%
  2. The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization (ICML 2011)
    Cited 1 time. 81.5%
  3. High-Performance Neural Networks for Visual Object Classification (2011)
    Cited 3 times. 80.49%
  4. Object Recognition with Hierarchical Kernel Descriptors (CVPR 2011)
    Cited 2 times. 80%
  5. 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
  6. Convolutional Deep Belief Networks on CIFAR-10 (2010)
    Cited 7 times. 78.9%
    Additional info: 2 layers
  7. Semiparametric Latent Variable Models for Guided Representation (2011)
    Cited 0 times. 77.9%
  8. Kernel Descriptors for Visual Recognition (NIPS 2010)
    Cited 6 times. 76%
    Additional info: KDES-A
  9. Image Descriptor Learning Using Deep Networks (2010)
    Cited 0 times. 75.18%
  10. Improved Local Coordinate Coding using Local Tangents (ICML 2010)
    Cited 8 times. 74.5%
    Additional info: Linear SVM with improved LCC
  11. Tiled convolutional neural networks (NIPS 2010)
    Cited 4 times. 73.1%
    Additional info: Deep Tiled CNNs (s=4, with finetuning)
  12. Semiparametric Latent Variable Models for Guided Representation (2011)
    Cited 0 times. 72.28%
    Additional info: Alpha = 0.01
  13. 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
  14. On Autoencoders and Score Matching for Energy Based Models (ICML 2011)
    Cited 0 times. 65.5%
  15. 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
  16. Learning invariant features through local space contraction (2011)
    Cited 0 times. 52.14%

3 comments:

Jacob said...

This is really great! I've been looking for something like this, and as far as I can see it is pretty complete.

rodrigob said...

The recently published "Selecting receptive fields in deep networks" http://www.stanford.edu/~acoates

reaches 82% on this dataset.

Hao Wooi Lim said...

@rodrigob: Thank you so much for the information. The blog is updated now :)