Why are some receptive fields larger?

Why are some receptive fields larger?

The receptive field size of neurons in primary visual cortex depends strongly on the stimulus contrast. The size can be more than two times larger when measured with low contrast stimuli than when measured with high contrast stimuli.

What is the meaning of receptive field size?

“…the receptive field size for the layer. This is the size (in pixels) of the local image region that affects a particular element in a feature map.” which makes sense with the traditional definition of a receptive field. Its usually thought as the number of pixels that affect a particular node in the feature map.

How do you increase the receptive field size?

The receptive field size of a unit can be increased in a number of ways. One option is to stack more layers to make the network deeper, which increases the receptive field size linearly by theory, as each extra layer increases the receptive field size by the kernel size.

What is a receptive field and what are its characteristics?

The receptive field encompasses the sensory receptors that feed into sensory neurons and thus includes specific receptors on a neuron as well as collectives of receptors that are capable of activating a neuron via synaptic connections.

What information does the brain use to determine the intensity of a stimulus?

How does the brain determine the intensity of a stimulus? By looking at the number of receptors activated and the frequency of action potentials from them. Also looks at the quality of the receptors that are activated. Receptors have different thresholds of activation – this can tell us how large the stimulus was.

How do we measure a receptive field?

Functional magnetic resonance imaging (fMRI) was used to estimate the average receptive field sizes of neurons in each of several striate and extrastriate visual areas of the human cerebral cortex.

When do you use the term receptive field?

Usually, when the receptive field term is mentioned, it is taking into consideration the final output unit of the network (i.e. a single unit on a binary classification task) in relation to the network input (i.e. input image of the network).

When to increase the size of the effective receptive field?

As we can see, the size of the effective receptive field is very dynamic and it is increased by a large margin after the training, which implies, as stated by authors of the paper, that better initialization schemes can be employed to increase the receptive field in the beginning of the training.

How does receptive field affect the value of an image?

Unlike in fully connected networks, where the value of each unit depends on the entire input to the network, in CNNs, anywhere in an input image outside the receptive field of a unit does not affect the value of that unit. Hence, it is necessary to carefully control the receptive field, to ensure that it covers the entire relevant image region.

How to calculate the effective receptive field in deep learning?

For instance, if we take an RGB CIFAR-10 image which has the input size of 32x32x3 (height, width, channels), wherein we have a receptive field (or the filter size) of 5×5 – then each neuron in the convolutional layer will have weights to a 5x5x3 region for every input image giving a total of 5*5*3 = 75 weights.