Towards Class-Specific Unit

by   Runkai Zheng, et al.

Class selectivity is an attribute of a unit in deep neural networks, which characterizes the discriminative ability of units to a specific class. Intuitively, decisions made by several highly selective units are more interpretable since it is easier to be traced back to the origin while that made by complex combinations of lowly selective units are more difficult to interpret. In this work, we develop a novel way to directly train highly selective units, through which we are able to examine the performance of a network that only rely on highly selective units. Specifically, we train the network such that all the units in the penultimate layer only response to one specific class, which we named as class-specific unit. By innovatively formulating the problem using mutual information, we find that in such a case, the output of the model has a special form that all the probabilities over non-target classes are uniformly distributed. We then propose a minimax loss based on a game theoretic framework to achieve the goal. Nash equilibria are proved to exist and the outcome is consistent with our regularization objective. Experimental results show that the model trained with the proposed objective outperforms models trained with baseline objective among all the tasks we test. Our results shed light on the role of class-specific units by indicating that they can be directly used for decisions without relying on low selective units.


page 1

page 7

page 8


Towards Interpretable Deep Networks for Monocular Depth Estimation

Deep networks for Monocular Depth Estimation (MDE) have achieved promisi...

Revisiting the Importance of Individual Units in CNNs via Ablation

We revisit the importance of the individual units in Convolutional Neura...

Selective Ensembles for Consistent Predictions

Recent work has shown that models trained to the same objective, and whi...

On the importance of single directions for generalization

Despite their ability to memorize large datasets, deep neural networks o...

Understanding the Importance of Single Directions via Representative Substitution

Understanding the internal representations of deep neural networks (DNNs...

Are there any 'object detectors' in the hidden layers of CNNs trained to identify objects or scenes?

Various methods of measuring unit selectivity have been developed with t...

Combinative Cumulative Knowledge Processes

We analyze Cumulative Knowledge Processes, introduced by Ben-Eliezer, Mi...

Please sign up or login with your details

Forgot password? Click here to reset