SimpleNet

Introduced by Hasanpour et al. in Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures

SimpleNet is a convolutional neural network with 13 layers. The network employs a homogeneous design utilizing 3 × 3 kernels for convolutional layer and 2 × 2 kernels for pooling operations. The only layers which do not use 3 × 3 kernels are 11th and 12th layers, these layers, utilize 1 × 1 convolutional kernels. Feature-map down-sampling is carried out using nonoverlaping 2 × 2 max-pooling. In order to cope with the problem of vanishing gradient and also over-fitting, SimpleNet also uses batch-normalization with moving average fraction of 0.95 before any ReLU non-linearity.

Source: Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures

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Papers

Paper	Code	Results	Date	Stars

Tasks

Task	Papers	Share
Image Classification	3	37.50%
Anomaly Detection	1	12.50%
Novelty Detection	1	12.50%
General Classification	1	12.50%
Saliency Prediction	1	12.50%
Object Detection	1	12.50%

Usage Over Time

This feature is experimental; we are continuously improving our matching algorithm.

Components

Component	Type	Add Remove
1x1 Convolution	Convolutions
Batch Normalization	Normalization
Convolution	Convolutions
Dropout	Regularization	(optional)
Max Pooling	Pooling Operations
ReLU	Activation Functions
Softmax	Output Functions

Categories

Add Remove

Convolutional Neural Networks