An Adaptive Locally Connected Neuron Model: Focusing Neuron
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We present a new artificial neuron model capable of learning its receptive field in the spatial domain of inputs. The name for the new model is focusing neuron because it can adapt both its receptive field location and size (aperture) during training. A network or a layer formed of such neurons can learn and generate unique connection structures for particular inputs/problems. The new model requires neither heuristics nor additional optimizations. Hence, all parameters, including those controlling the focus could be trained using the stochastic gradient descent optimization. We have empirically shown the capacity and viability of the new model with tests on synthetic and real datasets. We have constructed simple networks with one or two hidden layers; also employed fully connected networks with the same configurations as controls. In noise-added synthetic Gaussian blob datasets, we observed that focusing neurons can steer their receptive fields away from the redundant inputs and focused into more informative ones. Tests on common datasets such as MNIST have shown that a network of two hidden focusing layers can perform better (99.21 same configuration.
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