Image Classification On Mnist

Metrics

Percentage error

Results

Performance results of various models on this benchmark

		Paper Title	Repository
ProjectionNet	5.0	ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural Projections	-
Zhao et al. (2015) (auto-encoder)	4.76	Stacked What-Where Auto-encoders
DNN-2 (Trainable Activations)	3.6	Trainable Activations for Image Classification	-
DNN-3 (Trainable Activations)	3.0	Trainable Activations for Image Classification	-
DNN-5 (Trainable Activations)	2.8	Trainable Activations for Image Classification	-
PMM (Parametric Matrix Model)	2.62	Parametric Matrix Models	-
GECCO	1.96	A Single Graph Convolution Is All You Need: Efficient Grayscale Image Classification
Tsetlin Machine	1.8	The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic
Perceptron with a tensor train layer	1.8	Tensorizing Neural Networks
ANODE	1.8	Augmented Neural ODEs
MLP (ideal number of groups)	1.67	On the Ideal Number of Groups for Isometric Gradient Propagation	-
Weighted Tsetlin Machine	1.5	The Weighted Tsetlin Machine: Compressed Representations with Weighted Clauses
CNN Model by Som	1.41	Convolutional Sequence to Sequence Learning
Convolutional Clustering	1.4	Convolutional Clustering for Unsupervised Learning	-
LeNet 300-100 (Sparse Momentum)	1.26	Sparse Networks from Scratch: Faster Training without Losing Performance
Convolutional PMM (Parametric Matrix Model)	1.01	Parametric Matrix Models	-
BinaryConnect	1.0	BinaryConnect: Training Deep Neural Networks with binary weights during propagations	-
Explaining and Harnessing Adversarial Examples	0.8	Explaining and Harnessing Adversarial Examples
Sparse Activity and Sparse Connectivity in Supervised Learning	0.8	Sparse Activity and Sparse Connectivity in Supervised Learning	-
Deep Fried Convnets	0.7	Deep Fried Convnets

0 of 80 row(s) selected.

Command Palette

Image Classification On Mnist

Metrics

Results