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The big problem for neural network models which are trained to countinstances is that whenever test range goes high training range generalizationerror increases i.e. they are not good generalizers outside training range.Consider the case of automating cell counting process where more dense imageswith higher cell counts are commonly encountered as compared to images used intraining data. By making better predictions for higher ranges of cell count weare aiming to create better generalization systems for cell counting. Witharchitecture proposal of neural arithmetic logic units (NALU) for arithmeticoperations, task of counting has become feasible for higher numeric rangeswhich were not included in training data with better accuracy. As a part of ourstudy we used these units and different other activation functions for learningcell counting task with two different architectures namely Fully ConvolutionalRegression Network and U-Net. These numerically biased units are added in theform of residual concatenated layers to original architectures and acomparative experimental study is done with these newly proposed changes. Thiscomparative study is described in terms of optimizing regression loss problemfrom these models trained with extensive data augmentation techniques. We wereable to achieve better results in our experiments of cell counting tasks withintroduction of these numerically biased units to already existingarchitectures in the form of residual layer concatenation connections. Ourresults confirm that above stated numerically biased units does help models tolearn numeric quantities for better generalization results.

Systematically designing better instance counting models on cell images with Neural Arithmetic Logic Units