Image Classification On Mnist

Metrics

Percentage error

Results

Performance results of various models on this benchmark

		Paper Title
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

Command Palette

Image Classification On Mnist

Metrics

Results