Papers › Grouped Pointwise Convolutions Significantly Reduces Parameters in EfficientNet

Grouped Pointwise Convolutions Significantly Reduces Parameters in EfficientNet

14 Oct 202123rd International Conference of the Catalan Association for Artificial Intelligence 2021 10archive 2025-07-28

Joao Paulo Schwarz Schuler, Santiago Romani, Mohamed Abdel-Nasser, Hatem Rashwan, Domenec Puig

EfficientNet is a recent Deep Convolutional Neural Network (DCNN) architecture intended to be proportionally extendible in depth, width and resolution. Through its variants, it can achieve state of the art accuracy on the ImageNet classification task as well as on other classical challenges. Although its name refers to its efficiency with respect to the ratio between outcome (accuracy) and needed resources (number of parameters, flops), we are studying a method to reduce the original number of trainable parameters by more than 84% while keeping a very similar degree of accuracy. Our proposal is to improve the pointwise (1x1) convolutions, whose number of parameters rapidly grows due to the multiplication of the number of filters by the number of input channels that come from the previous layer. Basically, our tweak consists in grouping filters into parallel branches, where each branch processes a fraction of the input channels. However, by doing so, the learning capability of the DCNN is degraded. To avoid this effect, we suggest interleaving the output of filters from different branches at intermediate layers of consecutive pointwise convolutions. Our experiments with the CIFAR-10 dataset show that our optimized EfficientNet has similar learning capacity to the original layout when training from scratch.

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joaopauloschuler/kEffNet official report

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Tasks

Image Classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Image Classification	Oxford-IIIT Pet Dataset	kEffNet-B0	PARAMS	0.67M	#5 of 5	Archive leaderboard	report

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Methods

1x1 Convolution Average Pooling Batch Normalization Convolution DCNN Dense Connections Depthwise Convolution Depthwise Separable Convolution Dropout Grouped Convolution Groupwise Point Convolution Inverted Residual Block Pointwise Convolution RMSProp ReLU Sigmoid Activation Squeeze-and-Excitation Block

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