High-Performance Neural Networks for Visual Object Classification

Dan C. Cire\c{s}an , Ueli Meier , Jonathan Masci , Luca M. Gambardella , J\"urgen Schmidhuber

Authors on Pith no claims yet

classification 💻 cs.AI cs.NE

keywords classificationdeepepochserrormnistneuralnorbobject

read the original abstract

We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a supervised way. Our deep hierarchical architectures achieve the best published results on benchmarks for object classification (NORB, CIFAR10) and handwritten digit recognition (MNIST), with error rates of 2.53%, 19.51%, 0.35%, respectively. Deep nets trained by simple back-propagation perform better than more shallow ones. Learning is surprisingly rapid. NORB is completely trained within five epochs. Test error rates on MNIST drop to 2.42%, 0.97% and 0.48% after 1, 3 and 17 epochs, respectively.

This paper has not been read by Pith yet.

discussion (0)

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling
cs.LG 2026-05 unverdicted novelty 7.0

DRATS derives a minimax objective from a feasibility formulation of MTRL to adaptively sample tasks with the largest return gaps, leading to better worst-task performance on MetaWorld benchmarks.
SoK: A Comprehensive Analysis of the Current Status of Neural Tangent Generalization Attacks with Research Directions
cs.LG 2026-05 accept novelty 3.0

NTGA is the first clean-label generalization attack under black-box settings but is vulnerable to adversarial training and image transformations, with newer attacks outperforming it.