Research Article
Unsupervised Learning of Overlapping Image Components Using Divisive Input Modulation
Table 2
Details of the training procedure used for
each of the algorithms tested. In all cases the parameter values listed were
those found to produce the best results. Parameter values were kept constant
across variations in the task. All algorithms except use an online learning procedure. Hence, each weight update occurs after
an individual training image has been processed. This is described as a
training cycle. In contrast, uses a batch learning method.
Hence, each weight update is influenced by all training images. This is
described as a training epoch. Hence, with a set of 1000 training images (as
used in these experiments) an epoch is equivalent to 1000 training cycles for
the online learning algorithms. The third column specifies the number of
iterations used to determine the steady-state activations values. Weights were
initialised using random values selected from a Gaussian distribution with the
mean and standard deviation indicated. In each case initial weights with values
less than zero were made equal to zero.
| |||||||||||||||||||||||||||||||||||||||||||||