  Diverse regularization techniques have been developed such as L2
regularization, Dropout, DisturbLabel (DL) to prevent overfitting. DL, a
newcomer on the scene, regularizes the loss layer by flipping a small share of
the target labels at random and training the neural network on this distorted
data so as to not learn the training data. It is observed that high confidence
labels during training cause the overfitting problem and DL selects disturb
labels at random regardless of the confidence of labels. To solve this
shortcoming of DL, we propose Directional DisturbLabel (DDL) a novel
regularization technique that makes use of the class probabilities to infer the
confident labels and using these labels to regularize the model. This active
regularization makes use of the model behavior during training to regularize it
in a more directed manner. To address regression problems, we also propose
DisturbValue (DV), and DisturbError (DE). DE uses only predefined confident
labels to disturb target values. DV injects noise into a portion of target
values at random similar to DL. In this paper, 6 and 8 datasets are used to
validate the robustness of our methods in classification and regression tasks
respectively. Finally, we demonstrate that our methods are either comparable to
or outperform DisturbLabel, L2 regularization, and Dropout. Also, we achieve
the best performance in more than half the datasets by combining our methods
with either L2 regularization or Dropout.
