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Title: Relevant-features based Auxiliary Cells for Robust and Energy Efficient Deep Learning

Paper content:
Deep neural networks are complex non-linear models used as predictive analytics tool and have demonstrated state-of-the-art performance on many classification tasks.
However, they have no inherent capability to recognize when their predictions might go wrong.
There have been several efforts in the recent past to detect natural errors i.e.  misclassified inputs but these mechanisms pose additional energy requirements.
To address this issue, we present a novel post-hoc framework to detect natural errors in an energy efficient way.
We achieve this by appending relevant features based linear classifiers per class referred as Relevant features based Auxiliary Cells (RACs).
The proposed technique makes use of the consensus between RACs appended at few selected hidden layers to distinguish the correctly classified inputs from misclassified inputs.
The combined confidence of RACs is utilized to determine if classification should terminate at an early stage.
We demonstrate the effectiveness of our technique on various image classification datasets such as CIFAR10, CIFAR100 and Tiny-ImageNet.
Our results show that for CIFAR100 dataset trained on VGG16 network, RACs can detect 46% of the misclassified examples along with 12% reduction in energy compared to the baseline network while 69% of the examples are correctly classified.
Machine learning classifiers have achieved high performance on various classification tasks, e.g., object detection, speech recognition and image classification.
Decisions made by these classifiers can be critical when employed in real-world tasks such as medical diagnosis, self-driving cars, security etc.
Hence, identifying incorrect predictions i.e. detecting abnormal inputs and having a wellcalibrated predictive uncertainty is of great importance to AI safety.
Note that abnormal samples include natural errors, adversarial inputs and out-of-distribution (OOD) examples.
Natural errors are samples in the test data which are misclassified by the final classifier in a given network.
Various techniques have been proposed in literature to address the issue of distinguishing abnormal samples.
A baseline method for detecting natural errors and out-of-distribution examples utilizing threshold based technique on maximal softmax response was suggested by Hendrycks & Gimpel (2017) .
A simple unified framework to detect adversarial and out-of-distribution samples was proposed by Lee et al. (2018) .
They use activations of hidden layers along with a generative classifier to compute Mahalanobis distance (Mahalanobis, 1936) based confidence score.
However, they do not deal with detection of natural errors.
Bahat et al. (2019) ; Hendrycks & Gimpel (2017) ; Mandelbaum & Weinshall (2017) focus on detecting natural errors.
Mandelbaum & Weinshall (2017) use distance based confidence method to detect natural errors based on measuring the point density in the effective embedding space of the network.
More recently, Bahat et al. (2019) showed that KL-divergence between the outputs of the classifier under image transformations can be used to distinguish correctly classified examples from adversarial and natural errors.
To enhance natural error detection, they further incorporate Multi Layer Perceptron (MLP) at the final layer which is trained to detect misclassifications.
Most prior works on the line of error detection do not consider the latency and energy overheads that incur because of the detector or detection mechanism.
It is known that deeper networks expend higher energy and latency during feed-forward inference.
Adding a detector or detection mechanism on top of this will give rise to additional energy requirements.
The increase in energy may make these networks less feasible to employ on edge devices where reduced latency and energy with the ability to identify abnormal inputs is significant.
Many recent efforts toward energy efficient deep neural networks (DNNs) have explored early exit techniques.
Here, the main idea is to bypass (or turn off) computations of latter layers if the network yields high confidence prediction at early layers.
Some of these techniques include the adaptive neural networks (Stamoulis et al., 2018) , the edge-host partitioned neural network Ko et al. (2018) , the distributed neural network (Teerapittayanon et al., 2017) , the cascading neural network (Leroux et al., 2017) , the conditional deep learning classifier (Panda et al., 2016) and the scalable-effort classifier (Venkataramani et al., 2015) .
So far, there has been no unified technique that enables energy efficient inference in DNNs while improving their robustness towards detecting abnormal samples.
In this work, we target energy efficient detection of natural errors, which can be extended and applied to detecting OOD examples and adversarial data.
We propose adding binary linear classifiers at two or more intermediate (or hidden) layers of an already trained DNN and utilize the consensus between the outputs of the classifiers to perform early classification and error detection.
This idea is motivated from the following two observations:
• If an input instance can be classified at early layers Panda et al. (2016) then processing the input further by latter layers can lead to incorrect classification due to over-fitting.
This can be avoided by making early exit which also has energy benefits.
• We have observed that on an average, the examples which are misclassified do not have consistent hidden representations compared to correctly classified examples.
The additional linear classifiers and their consensus enables identifying this inconsistent behaviour to detect misclassified examples or natural errors.
The training and construction of the linear classifiers is instrumental towards the accurate and efficient error detection with our approach.
We find that at a given hidden layer, the error detection capability (detecting natural errors) is higher if we use class-specific binary classifiers trained on the corresponding relevant feature maps from the layer.
In fact, using a fully connected classifier trained on all feature maps (conventionally used in early exit techniques of Panda et al. (2016) ) does not improve error detection capability.
Training these binary classifiers on relevant features can be considered as encoding prior knowledge on the learned hidden feature maps, thereby, yielding better detection capability.
Besides improved error detection, a key advantage of using class wise binary linear classifiers trained on only relevant features is that they incur less overhead in terms of total number of parameters, as compared to a fully connected classifier trained on all feature maps.
We use "relevant features" and class specific classifiers instead of using all the feature maps and one fully connected classifier at a hidden layer for the following reasons:
(a) We have observed that at a given hidden layer, the detection capability (detecting natural errors) is higher if we use relevant feature maps with class-specific binary classifiers than using a fully connected classifier trained on all feature maps.
Training these binary classifiers on relevant features can be considered as encoding prior knowledge on the learned hidden features maps and hence is expected to have better detection capability.
(b) The class wise binary linear classifiers trained on relevant features have less number of parameters compared to a fully connected classifier trained on all the feature maps.
In the proposed framework, class-specific binary linear classifiers are appended at few selected hidden layers which have maximal information.
These hidden layers are referred to as validation layers.
The set of all binary linear classifiers at a validation layer constitute a Relevant feature based Auxiliary Cell (RAC).
We use the consensus of RACs to detect natural errors which improves the robustness of the network.
The combined confidence of RACs is used to perform early classification that yields energy efficiency.
Deep neural networks are crucial for many classification tasks and require robust and energy efficient implementations for critical applications.
In this work, we device a novel post-hoc technique for energy efficient detection of natural errors.
In essence, our main idea is to append class-specific binary linear classifiers at few selected hidden layers referred as Relevant features based Auxiliary Cells (RACs) which enables energy efficient detection of natural errors.
With explainable techniques such as Layerwise Relevance Propagation (LRP), we determine relevant hidden features corresponding to a particular class which are fed to the RACs.
The consensus of RACs (and final classifier if there is no early termination) is used to detect natural errors and the confidence of RACs is utilized to decide on early classification.
We also evaluate robustness of DNN with RACs towards adversarial inputs and out-of-distribution samples.
Beyond the immediate application to increase robustness towards natural errors and reduce energy requirement, the success of our framework suggests further study of energy efficient error detection mechanisms using hidden representations.

assistant: text: Improve the robustness and energy efficiency of a deep neural network using the hidden representations.