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What do you mean? Any image classifier will use way more than 4 kernels for convolution. All those layers are interlaced. Furthermore they also contain fully co
by isaacfrond 4y ago
What do you mean? Any image classifier will use way more than 4 kernels for convolution. All those layers are interlaced. Furthermore they also contain fully connected layers, with neuron integrating way more than 10^3 signals.
The reasons that there aren't much more fully connected layers, is that this doesn't work. Actually, one of the key developments in NNs is architectural, minpools, ReLus, U-net. All are key for modern networks, all architectural.
- danielheath 4y agoSorry, that was hastily written & could have been clearer. It is, of course, very common to perform (eg) convolution with a larger kernel size, or to use a dense layer. However, unlike wet-neurons: * Convolution has the same local shape for each cell. * Convolution has no self-suppression for recent activation, vs time-dependent, nonlinear response in wet cells. * Current silicon offers no performance advantage for interconnect to adjacent cells (could be done). With ~80 billion neurons in a brain, 1000 is not like a dense layer at all.