Reducing Network Agnostophobia
Agnostophobia, the fear of the unknown, can be experienced by deep learning engineers while applying their networks to real-world applications. Unfortunately, network behavior is not well defined for inputs far from a networks training set. In an uncontrolled environment, networks face many instances that are not of interest to them and have to be rejected in order to avoid a false positive. This problem has previously been tackled by researchers by either a) thresholding softmax, which by construction cannot return none of the known classes, or b) using an additional background or garbage class. In this paper, we show that both of these approaches help, but are generally insufficient when previously unseen classes are encountered. We also introduce a new evaluation metric that focuses on comparing the performance of multiple approaches in scenarios where such unseen classes or unknowns are encountered. Our major contributions are simple yet effective Entropic Open-Set and Objectosphere losses that train networks using negative samples from some classes. These novel losses are designed to maximize entropy for unknown inputs while increasing separation in deep feature space by modifying magnitudes of known and unknown samples. Experiments on networks trained to classify classes from MNIST and CIFAR-10 show that our novel loss functions are significantly better at dealing with unknown inputs from datasets such as Devanagari, NotMNIST, CIFAR-100, and SVHN.
# 5501 # Update based on the Authors Response Thank you for the clarification. Based on the response I am willing to vote for an accept.  # Summary This paper proposes a new approach to improve the model’s robustness against unknown classes. The authors propose a new objective function that, unlike common detection algorithms, does not add the background class to softmax. Instead, for a given background example, the objective function maximizes the entropy of the softmax layer, which leads to a low magnitude feature representation for the background sample.   In addition, the authors propose the use of objectosphere loss to distinguish the known/unknown classes further by penalizing the feature representation of the background sample so that the network does not respond to unknown objects.   Theoretical and empirical proof demonstrate the potential capability of the proposed approach.  # Quality The authors define the problem well and provide a thorough literature review. The theoretical background is also well explained in the method section. Experimentation also shows the capability of the proposed approach.  I would want to see one more set of experiment, in which the proposed approach is used within a real detection pipeline such as, as described in the paper RPN of F-RCNN.   # Clarity This is a well-written paper. It flows well, and easy to navigate.  It is not explicitly stated in the paper whether the author will release their implementation for the reproducibility of the paper.  # Significance The problem defined in the paper is significant especially within the real-world applications of object detection algorithms due to excessive exposure to unknown classes in real-world scenarios. The paper’s solution is significant to tackle these issues.