Reducing Network Agnostophobia (1811.04110v2)

Abstract: 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.

• The paper introduces two novel loss functions, Entropic Open-Set loss and Objectosphere loss, to clearly separate known and unknown inputs in the feature space.
• It presents the OSCR curve as a new metric to effectively evaluate classification performance in open-set scenarios.
• Experimental results on MNIST, CIFAR-10, and other datasets show significant improvements in reducing false positives while maintaining high accuracy on known classes.

An Insightful Overview of "Reducing Network Agnostophobia"

The paper "Reducing Network Agnostophobia" by Akshay Raj Dhamija, Manuel G\"unther, and Terrance E. Boult addresses a significant challenge in the domain of deep learning and computer vision: handling unknown inputs or classes during inference. This issue, commonly referred to as "agnostophobia" in neural networks, arises when models are deployed in real-world scenarios where inputs may not belong to any of the known classes that were present during training. Traditional methods like thresholding softmax outputs or incorporating a background class have limitations, especially in open-set recognition scenarios.

Key Contributions

The paper introduces two novel loss functions—Entropic Open-Set loss and Objectosphere loss—aimed at improving the network's ability to reject unknown inputs while maintaining robust classification on known classes. The authors propose these loss functions to achieve better separation in feature space between known and unknown inputs. Additionally, they present a new evaluation metric, the Open-Set Classification Rate (OSCR) curve, facilitating a more meaningful comparison of different approaches in the presence of unknowns.

1. Entropic Open-Set Loss: This loss function maximizes the entropy of the softmax scores for inputs from unknown classes during training. The goal is to achieve uniform probability distribution over known classes, thereby increasing the softmax entropy and making the network indifferent to unknown inputs.
2. Objectosphere Loss: Building upon the Entropic Open-Set loss, this loss additionally minimizes the Euclidean norm of deep features for unknown inputs while ensuring a minimum magnitude for known classes. This facilitates a clear separation in feature space, differentiating between responses for known and unknown inputs.
3. Open-Set Classification Rate (OSCR) Curve: This metric evaluates the performance of classification networks in open-set scenarios by plotting the Correct Classification Rate (CCR) against the False Positive Rate (FPR), offering a comprehensive view of how effectively a network can distinguish between known and unknown inputs.

Experimental Evidence

Experiments conducted on MNIST, CIFAR-10, and other datasets demonstrate the efficacy of the proposed loss functions. Networks trained with Entropic Open-Set and Objectosphere losses outperform standard softmax and other methods like OpenMax, particularly in open-set classification scenarios. The results indicate significant improvements in rejecting samples from unknown classes, with better OSCR curves highlighting the reduced false positive rates while maintaining high recognition rates on known classes.

Theoretical and Practical Implications

Theoretically, this paper advances the understanding of modeling unknowns in neural network training, emphasizing the importance of feature representation in handling out-of-distribution samples. Practically, this work suggests that deploying networks with these loss functions can result in systems more resilient to false positives in real-world applications, such as autonomous driving or security systems, where encountering novel inputs is common.

Future Directions

The introduction of Entropic Open-Set and Objectosphere losses opens several avenues for future research. Exploring their integration into more complex architectures or hybrid approaches with existing uncertainty estimation techniques could further enhance network robustness. Additionally, investigating the application of these losses across different modalities and tasks, beyond image classification, including natural language processing and time series analysis, could yield valuable insights into the generalizability of the approach.

Overall, the paper "Reducing Network Agnostophobia" provides a substantive contribution to the field of open-set recognition, presenting practical methodologies and a robust theoretical foundation. Its implications for enhancing neural network performance in uncertain environments are profound, providing a crucial step forward in the quest for more intelligent and adaptable AI systems.