  Deep Convolutional Neural Networks (CNN) enforces supervised information only
at the output layer, and hidden layers are trained by back propagating the
prediction error from the output layer without explicit supervision. We propose
a supervised feature learning approach, Label Consistent Neural Network, which
enforces direct supervision in late hidden layers. We associate each neuron in
a hidden layer with a particular class label and encourage it to be activated
for input signals from the same class. More specifically, we introduce a label
consistency regularization called "discriminative representation error" loss
for late hidden layers and combine it with classification error loss to build
our overall objective function. This label consistency constraint alleviates
the common problem of gradient vanishing and tends to faster convergence; it
also makes the features derived from late hidden layers discriminative enough
for classification even using a simple $k$-NN classifier, since input signals
from the same class will have very similar representations. Experimental
results demonstrate that our approach achieves state-of-the-art performances on
several public benchmarks for action and object category recognition.
