Title: INHIBITED SOFTMAX FOR UNCERTAINTY ESTIMATION IN NEURAL NETWORKS

Abstract: We present a new method for uncertainty estimation and out-of-distribution detection in neural networks with softmax output. We extend softmax layer with an additional constant input. The corresponding additional output is able to represent the uncertainty of the network. The proposed method requires neither additional parameters nor multiple forward passes nor input preprocessing nor out-of-distribution datasets. We show that our method performs comparably to more computationally expensive methods and outperforms baselines on our experiments from image recognition and sentiment analysis domains.

1. INTRODUCTION:

The applications of computational learning systems might cause intrusive effects if we assume that predictions are always as accurate as during the experimental phase. Examples include misclassified traffic signs (Evtimov et al., 2018) and an image tagger that classified two African Americans as gorillas (Curtis, 2015) . This is often caused by overconfidence of models that has been observed in the case of deep neural networks (Guo et al., 2017) . Such malfunctions can be prevented if we estimate correctly the uncertainty of the machine learning system. Beside AI safety, uncertainty is useful in the active learning setting in which data collection process is expensive or time consuming (Houlsby et al., 2011; Rottmann et al., 2018) .

While uncertainty estimation in neural networks is an active field of research, the current methods are rarely adopted. It is desirable to develop a method that does not create an additional computational overhead. Such a method could be used in environments that focus on quick training and/or inference. If such a method is simple, the ease of implementation should encourage practitioners to develop danger-aware systems in their work.

We suggest a method that measures uncertainty of the neural networks with a softmax output layer. We replace this layer with Inhibited Softmax layer (Saito et al., 2016) , and we show that it can be used to express the uncertainty of the model. In our experiments the method outperforms baselines and performs comparably with more computationally expensive methods on the out-of-distribution detection task.

We contribute with:

• The mathematical explanation why the additional Inhibited Softmax output can be interpreted as an uncertainty measure.

• The additions to the Inhibited Softmax that improve its uncertainty approximation properties.

• The benchmarks comparing Inhibited Softmax, baseline and contemporary methods for measuring uncertainty in neural networks.

2. RELATED WORK:

Certainty of classification models can be represented by maximum of probabilities (Hendrycks & Gimpel, 2017) . It has been shown, however, that deep neural networks are prone to the overconfidence problem (Guo et al., 2017) , and thus so simple a method might not measure certainty well.

The modern Bayesian Neural Networks (Blundell et al., 2015; Hernández-Lobato & Adams, 2015; Louizos & Welling, 2017; Malinin & Gales, 2018; Wang et al., 2016; Hafner et al., 2018; Zhang et al., 2018; Khan et al., 2018) aim to confront this issue by inferring distribution over the models' weights. This approach has been inspired by Bayesian approaches suggested as early as the nineties (Buntine & Weigend, 1991; Neal, 1993) . A very popular regularisation mean -dropout -also can be a source of approximate Bayesian inference (Gal & Ghahramani, 2016) . Such technique, called Monte Carlo dropout (Gal & Ghahramani, 2015) , belongs to the Bayesian Neural Networks class and has been since used in the real-life scenarios (e.g. Leibig et al., 2017) . In the Bayesian Neural Networks the uncertainty is modelled by computing the predictive entropy or mutual information over the probabilities coming from stochastic predictions (Smith & Gal, 2018) .

Other methods to measure uncertainty of neural networks include a non-Bayesian ensemble (Lakshminarayanan et al., 2017) , a student network that approximates the Monte Carlo posterior predictive distribution (Korattikara Balan et al., 2015) , modelling Markov chain Monte Carlo samples with a GAN (Wang et al., 2018) , Monte Carlo Batch Normalization (Teye et al., 2018) and the nearest neighbour analysis of penultimate layer embedding (Mandelbaum & Weinshall, 2017) .

The concept of uncertainty is not always considered as a homogeneous whole. Some of the authors distinguish two types of uncertainties that influence predictions of machine learning models (Kendall & Gal, 2017) : epistemic uncertainty and aleatoric uncertainty. Epistemic uncertainty represents the lack of knowledge about the source probability distribution of the data. This uncertainty can be reduced by increasing the size of the training data. Aleatoric uncertainty arises from homoscedastic, heteroscedastic and label noises and cannot be reduced by the model. We will follow another source (Malinin & Gales, 2018 ) that defines the third type: distributional uncertainty. It appears when the test distribution differs from the training distribution, i.e. when new observations have different nature then the ones the model was trained on.

A popular benchmark for assessing the ability of the models to capture the distributional uncertainty is distinguishing the original test set from out-of-distribution dataset (Hendrycks & Gimpel, 2017) .

There are works that focus only on this type of uncertainty (Lee et al., 2018) . ODIN (Liang et al., 2018) does not require changing already existing network and relies on gradient-based input preprocessing. Another work (DeVries & Taylor, 2018) is close to the functionality of our method, as it only adds a single densely connected layer and uses a single forward pass for a sample.

Bayesian neural networks are more computationally demanding as they usually require multiple stochastic passes and/or additional parameters to capture the priors specification.

To the best of our knowledge, our method is the first that improves upon the baseline, and meets all the following criteria:

• No additional learnable parameters required.

• Only single forward pass needed.

• No additional out-of-distribution or adversarial observations required.

• No input preprocessing.

The technique we use, Inhibited Softmax, has been successfully used for the prediction of background class in the task of extraction the objects out of aerial imagery (Saito et al., 2016) . The original work does not mention other possible applications of this softmax modification.

3. INHIBITED SOFTMAX:

In this section we will define the Inhibited Softmax function. We will provide mathematical rationale on why it can provide uncertainty estimation when used as the output function of a machine learning model. Later we will present several adjustments which we have made to the model architecture when applying Inhibited Softmax to a multilayer neural network.

Inhibited Softmax function IS c is given by: Let x ∈ R n and c ∈ R, then IS c is a function which maps R n to R n . The i-th output is equal to:

EQUATION

Following equation holds:

EQUATION

where:

EQUATION

and S(x) is the standard softmax function applied to vector x. We will later refer to P c (x) as "certainty factor". Now let's assume that IS c is the output of a multiclass classification model trained with the crossentropy loss function l IS . Assuming that the true class of a given example is equal to t the loss is equal to:

EQUATION

where l S is the cross-entropy loss function for a model with a standard softmax output. As one may see -the optimisation process both minimises classification error (given by l S ) and maximises the certainty factor P c (x) for all training examples. This is the intuition that explains why Inhibited Softmax serves as an uncertainty estimator -as P c (x) is maximised only for cases from training distribution.

If P c estimates the certainty of the model, in order to provide a valid uncertainty score we will introduce the following function:

EQUATION

which is minimised during the optimisation process. It is worth to mention that it might be interpreted as an artificial softmax output from the additional channel.

3.1. ADJUSTMENTS AND REGULARISATION:

Although P u is minimised during the optimisation process we would like to ensure that its low values are obtained solely because of the training process and neither because of the trivial solutions nor accidental network structure. Because of that we applied the following network adjustments:

• removing bias terms from the inhibited softmax layer. This was done in order to prevent the network from minimising P u (x) that can be achieved by increasing the values of bias terms which are independent of data. • changing the activation function to a kernel function in the penultimate layer of the network.

The main aim of this adjustment was to make activations of the layer noticeably greater from 0 only for a narrow, learnable region in the input space of the penultimate network layer. Therefore, the activations of that layer corresponding to out-of-domain examples are likely to be close to 0, which, combined with the lack of bias term, results in vanishing input to IS.

In order to combat the overconfidence of the network we:

• add the activity regularisation. Standard softmax classification is invariant to translation along the all-ones vector. On the other hand, -log P c is a decreasing function of x i . As l is is a sum of standard classification error and -log P c increasing all values of x i by a constant decreases the loss, which causes gradient optimisation methods to increase x i boundlessly. In order to address this issue we introduced the following regularisation method:

EQUATION

where the gradient of the additional term is parallel to the all-ones vector and thus does not affect the standard softmax classification loss.

• apply l 2 regularisation to the weights of the output layer. It indirectly limits the values of x i , as we use a bounded activation function.

These adjustments significantly increased the certainty estimation properties of Inhibited Softmax.

The dependency between performance and applying these changes to model architecture is presented in Appendix 1.

4. EXPERIMENTS:

We have compared various ways of estimating uncertainty in neural networks (hereinafter referred to as "methods"). For the benchmarks we implement these methods on top of the same base neural network. We use following experiments to check their quality:

• Out-of-distribution (OOD) examples detection -following (Hendrycks & Gimpel, 2017) we use ROC AUC and average precision (AP) metrics to check the classifier's ability to distinguish between the original test set and a dataset coming from another probability distribution. This experiments show whether the method measures well the distributional uncertainty on a small sample of out-of-distribution datasets.

• Predictive performance experiment -given a dataset, we split it into train, test and validation sets. We report accuracy and negative log loss on the test set. Any method should not deteriorate predictive performance of the network.

• Wrong prediction detection -we expect that the more confident the model is, the more accurate its predictions on in-distribution dataset should be. In this experiment the ground truth labels are used to construct two classes after the prediction on the test dataset is performed. The classes represent the correctness of the classifier prediction. Then, the uncertainty measure is used to compute TPRs and FPRs. We report ROC AUC scores on this setting. This experiment shows whether the method measures well the combination of epistemic and aleatoric uncertainty on a small sample of datasets. In this experiment we do not report average precision score, as it would be distorted by different levels of misclassification in the predictions. (Gal & Ghahramani, 2016) predictive entropy of the probabilities from 50 stochastic forward passes MCD Bayes By Backprop with a Gaussian prior (Blundell et al., 2015) predictive entropy of the probabilities from 10 stochastic forward passes Table 1 shows the methods and respective uncertainty measures that will be benchmarked 1 . We establish two baselines. Both of them work on the unmodified base neural network, but uncertainty is measured in different ways, using either the maximum of probabilities over classes or entropy of probabilities. The method we suggest to use is referred as IS.

Method:

We have chosen these methods as they have been already used for benchmarking (e.g. Louizos & Welling, 2017) , and they are well-known in the Bayesian Neural Network community. In the case of Inhibited Softmax we set l2 penalty to 0.01, activity regularisation to 10 -6 , c to 1 and we use rescaled Cauchy distribution's PDF (f (x) = 1 1+x 2 ). The datasets 2 and the respective base neural networks we have chosen for the experiments are reported in Table 2 .

The base network for CIFAR-10 consists of 3 2D convolutional layers with 2D batch norm and 0.25 dropout. The convolving filter size was 3. Each convolutional layer was followed by 2D maximum pooling over 3x3 neurons with stride 2. The number of filters in the consecutive layers are 80, 160 and 240. Then there are 3 fully-connected layers. After the first fully-connected layer we apply 0.25 dropout. The number of neurons in the consecutive dense layers are 200, 100, 10.

In the experiments we report averages over three training and prediction procedures on the same training-test splits.

In computer vision OOD tasks, Inhibited Softmax improves upon baselines with an exception of the task of discriminating MNIST from black and white CIFAR-10 (Table 3 ). Our method still achieves very high detection performance (0.996 ROC AUC and 0.999 AP). This dataset is the least similar to MNIST. In contrast to other tested datasets against the digit recognition networks, various shades of gray dominate the images. IS is better than BASE on NOTMNIST (0.977 ROC AUC vs 0.958) and Omniglot (0.97 ROC AUC vs 0.956). IS' ROC AUC performance on MNIST/NOTMNIST and CIFAR-10/SVHN is similar to MCD (resp. 0.977 vs 0.974 and 0.923 vs 0.927). IS achieves the best result on CIFAR-10/LFW-A task and all the methods vastly outperform the baselines.

Inhibited Softmax improves upon other methods on the sentiment analysis task. Especially large improvement can be observed on the test against the Movie Reviews dataset. For example, the ROC AUC of IS (0.875) is much greater than ROC AUC of MCD (0.836). Methods other than IS are not much better than the baseline (BBP's 0.845 ROC AUC), sometimes being worse (DE's 0.835 ROC AUC). IS is also the best on the test against Reuters-21578 and Customer reviews (resp. 0.822 and 0.731). Two baselines achieve the same results on sentiment analysis experiment as there is no difference in ranking of the examples between the chosen uncertainty measures. We do not corroborate the results from the baseline publication (Hendrycks & Gimpel, 2017) . We discovered that in that paper the out-of-distribution samples for Movie Reviews were constructed by taking single lines from the dataset file, while the reviews span over few lines. Our results show that the x ~x (no bias in last layer)

x ~2x 5x

Table 4 : Predictive performance experiment results and computational overhead. Only the baseline and Inhibited Softmax have neither additional parameters nor require multiple forward passes. The green color shows the best results, the red -results worse than the baseline. We compare Inhibited Softmax and baselines with methods that require more forward passes and/or more params.

detection is a tougher task when full reviews are used (BASE achieves 0.837 ROC AUC vs 0.94 ROC AUC (Hendrycks & Gimpel, 2017) ).

To understand where the improvements of IS in the sentiment OOD tasks come from, we trained the base network with the same regularisation consisting of l2 penalty on weights and the activity regularizer. Such an improved baseline achieved 0.853 on IMDB/Movie Reviews and 0.838 on IMDB/Reuters-21578. Both results are better than all the methods but IS, with the latter improving also upon IS. On the other hand, this enhanced baseline did not improve on IMDB/Customer Reviews achieving 0.712 ROC AUC.

In our experiments Inhibited Softmax does not deteriorate the predictive performance of the neural network (Table 4 ). Its accuracy was similar to the baselines on every task, for example on IMDB dataset the accuracy is 0.04% lower and on CIFAR-10 0.19% higher. Ensembling the networks gives the best predictive performance. We observed that text models perform very well on Movie Reviews dataset. Despite coming from a different probability distribution the latter dataset contains strong sentiment retrieved by the networks for the prediction of the correct label.

Wrong prediction detection results ( In practice, the overlap of the correctly detected out-of-distribution observations between Inhibited Softmax and Bayesian methods is surprisingly large. To demonstrate it, we compare Monte Carlo dropout and our method on an experiment from (Smith & Gal, 2018) . We train a fully connected variational autoencoder (VAE) on the MNIST dataset. Then, we create a grid in the latent space and for each point we generate a sample. We plot the uncertainty estimation of the methods on generated samples from these points together with labelled latent encoding of the test samples (Figure 1 ). Both methods are unable to detect out of distribution samples generated from the bottom left corner of the 2D latent space. Another example for similarity is that both of the methods do not estimate high uncertainty in area where blue and purple classes intersect in the latent space. This leads to a hypothesis that there exist samples that are tougher to detect by uncertainty measures for any recently proposed method. Similarly to the ideas from adversarial attacks field, it might be worth to investigate how to construct such samples. We believe it might be a way to improve uncertainty sampling performance.

6. FURTHER WORK & LIMITATIONS:

We notice that working on following aspects can enhance the uncertainty estimation:

• Developing an analogous to IS method for regression.

• Limiting the number of required hyperparameters for Inhibited Softmax.

• Expanding the method to hidden layers. This is especially promising as the Inhibited Softmax performs better than other methods on a shallow network in our sentiment analysis experiment. On deeper networks IS has not have yet such advantage and it might be possible to outperform other methods.

Although we showed by experiments that the architecture adjustments applied to the network architecture are beneficial, we are still lacking the full and sound mathematical explanation of their influence on model behaviour. Such analysis could result in both better procedure for setting Inhibited Softmax hyperparameters as well as new adjustments to the network structure.

7. CONCLUSION & DISCUSSION:

We presented a new method for uncertainty estimation -Inhibited Softmax. The method can be easily applied to various multilayer neural network architectures and does not require additional parameters, multiple stochastic forward passes or OOD examples.

The results show that the method outperforms baseline and performs comparably to the other methods. The method does not deteriorate predictive performance of the classifier.

The predictive performance from IMDB/Movie Reviews experiment suggests that even if the observation comes from another probability distribution and the uncertainty measure is able to detect it, the network can still serve as a useful classifier.

The improvement of the baseline on the sentiment task after adding suggested regularisation indicates it might be worth to apply such measures to other uncertainty estimation methods.

• Deep Ensembles -we skipped adversarial training, as adversarial training is a way to improve performance of any of the methods used in the paper. We use an ensemble of 5 base networks.

• Monte Carlo Dropout -for MNIST we use dropout probability 0.25 on all but last layers, 0.5 on the only trainable layer in the sentiment experiment, and on CIFAR-10 network 0.25 only on the last but one layer. In larger networks setting dropout on many layers required greater number of epochs to achieve top performance. We run 50 forward passes for variational prediction.

• Bayes By Backprop -we observed that there is a trade-off between accuracy and OOD detection performance that depends on the initialisation of the variance. We initialisation that led to the best combination of accuracy and OOD detection performance in our view. We run 10 forward passes for variational prediction.

We followed the original publications when possible. For example, the number of networks in DE and number of inferences in BBP and MCD are taken from the original descriptions of the algorithms.

APPENDIX 3 -VISUALIZATION

In the visualisation section of the paper the uncertainties were normalised so that the predictive entropy and IS' probabilities could be visually compared. The normalisation for a method was performed by ranking the uncertainties and splitting them into 400 equal bins. Then, the bins are plotted. White colour represents the bin with the most uncertainty, the black -with the least.

For better understanding of the latent space we visualise the images decoded from the grid from the latent space (Figure 3 ). 

:



Figures and Tables:

Figure 1: Visualisation of uncertainty measures: Inhibited Softmax (top) and Monte Carlo Dropout (bottom) on the VAE's latent space. The shade of grey represent the normalised uncertainty on the samples generated from the latent space. The lighter the more uncertainty. The points represent the encoded test set and the colours are the classes. The axes show coordinates in the latent space. Note the similarity in the regions between the methods.

Figure 3: Visualisation of the images generated from the grid in the latent space (right) next to the uncertainty measure visualisation (left).

methods used for benchmarks. Both the base network methods will serve as baselines.

Table content: <table><tr><td>BBP</td></tr></table>

Datasets and neural architectures used.

Table content: <table/>

The choice of hyperparameters and training details for methods other than Inhibited Softmax is further discussed in the appendix 2 Preprocessing is discussed in the appendix

Table content: <table><tr><td colspan="3">Datasets (In/Out) Score MCD</td><td>IS</td><td colspan="2">BASE BASEE</td><td>BBP</td><td>DE</td></tr><tr><td>MNIST/</td><td>ROC</td><td>0.974</td><td>0.977</td><td>0.958</td><td>0.956</td><td>0.982</td><td>0.979</td></tr><tr><td>NOTMNIST</td><td>AP</td><td>0.984</td><td>0.982</td><td>0.938</td><td>0.955</td><td>0.989</td><td>0.988</td></tr><tr><td>MNIST/</td><td>ROC</td><td>0.999</td><td>0.996</td><td>0.997</td><td>0.996</td><td>0.999</td><td>0.999</td></tr><tr><td>CIFAR-10 B&amp;W</td><td>AP</td><td colspan="3">0.9997 0.9992 0.9994</td><td>0.999</td><td colspan="2">0.9997 0.9997</td></tr><tr><td>MNIST/</td><td>ROC</td><td>0.977</td><td>0.97</td><td>0.956</td><td>0.953</td><td>0.975</td><td>0.977</td></tr><tr><td>Omniglot</td><td>AP</td><td>0.992</td><td>0.99</td><td>0.983</td><td>0.981</td><td>0.991</td><td>0.99</td></tr><tr><td>CIFAR-10/</td><td>ROC</td><td>0.927</td><td>0.923</td><td>0.866</td><td>0.865</td><td>0.913</td><td>0.946</td></tr><tr><td>SVHN</td><td>AP</td><td>0.987</td><td>0.984</td><td>0.961</td><td>0.958</td><td>0.981</td><td>0.99</td></tr><tr><td>CIFAR-10/</td><td>ROC</td><td>0.693</td><td>0.775</td><td>0.593</td><td>0.594</td><td>0.723</td><td>0.755</td></tr><tr><td>LFW-A</td><td>AP</td><td>0.142</td><td>0.194</td><td>0.127</td><td>0.126</td><td>0.169</td><td>0.181</td></tr><tr><td>IMDB/</td><td>ROC</td><td>0.723</td><td>0.731</td><td>0.717</td><td>0.717</td><td>0.729</td><td>0.718</td></tr><tr><td colspan="2">Customer Reviews AP</td><td>0.027</td><td>0.088</td><td>0.027</td><td>0.027</td><td>0.028</td><td>0.026</td></tr><tr><td>IMDB/</td><td>ROC</td><td>0.836</td><td>0.875</td><td>0.837</td><td>0.837</td><td>0.845</td><td>0.835</td></tr><tr><td>Movie Reviews</td><td>AP</td><td>0.755</td><td>0.875</td><td>0.756</td><td>0.756</td><td>0.769</td><td>0.753</td></tr><tr><td>IMDB/</td><td>ROC</td><td>0.817</td><td>0.822</td><td>0.816</td><td>0.816</td><td>0.805</td><td>0.815</td></tr><tr><td>Reuters-21578</td><td>AP</td><td>0.735</td><td>0.818</td><td>0.727</td><td>0.727</td><td>0.715</td><td>0.724</td></tr></table>

Out of distribution detection results. The green colour shows the best results, the redresults worse than any of the baselines.

Table content: <table><tr><td/><td/><td>MCD</td><td>IS</td><td colspan="2">BASE(E) BBP</td><td>DE</td></tr><tr><td>MNIST</td><td colspan="2">Accuracy 0.992 NLL 0.034</td><td>0.992 0.028</td><td>0.992 0.035</td><td colspan="2">0.991 0.994 0.031 0.019</td></tr><tr><td>CIFAR10</td><td colspan="2">Accuracy 0.854 NLL 0.527</td><td>0.853 0.668</td><td>0.851 1.661</td><td colspan="2">0.841 0.88 0.514 0.385</td></tr><tr><td>IMDB</td><td colspan="2">Accuracy 0.883 NLL 0.291</td><td>0.882 0.305</td><td>0.883 0.295</td><td colspan="2">0.882 0.885 0.302 0.289</td></tr><tr><td>IMDB model</td><td colspan="2">Accuracy 0.848</td><td>0.851</td><td>0.857</td><td colspan="2">0.849 0.851</td></tr><tr><td colspan="2">on Movie Reviews NLL</td><td>0.378</td><td>0.345</td><td>0.362</td><td colspan="2">0.365 0.586</td></tr><tr><td colspan="2">Number of forward passes</td><td>50</td><td>1</td><td>1</td><td>10</td><td>1</td></tr><tr><td>Params (vs BASE)</td><td/><td/><td/><td/><td/></tr></table>

Table5) show that IS is the only method that is able to detect misclassified observations better than a random classifier (0.687 ROC AUC) on the sentiment task. Wrong prediction detection results (ROC AUC). The green color shows the best results, the red -results worse than any of the baselines.

Table content: <table><tr><td>Dataset</td><td>MCD</td><td>IS</td><td colspan="3">BASE BASEE BBP</td><td>DE</td></tr><tr><td>MNIST</td><td colspan="3">0.982 0.982 0.979</td><td>0.979</td><td>0.979 0.987</td></tr><tr><td colspan="4">CIFAR-10 0.869 0.855 0.875</td><td>0.877</td><td>0.878 0.886</td></tr><tr><td>IMDB</td><td colspan="3">0.418 0.687 0.501</td><td>0.501</td><td>0.398 0.391</td></tr></table>

Peer review (iclr2019):
 The authors present inhibited softmax, a modification of the softmax through adding a constant activation which under a particular interpretation provides a measure for uncertainty. The method has no learnable parameter, works with only a single forward operation, does not need additional data for training, and does no input "preprocessing." The authors present experiments on vision problems and sentiment analysis. The idea is to add a constant pre-softmax class activation c. In section 3 the authors explain why this "serves as an uncertainty estimator." I read this paragraph several times, I do not see how they reach this conclusion. They point out that "P_c(x) is maximized only for cases from training distribution." Unless we have a reasonable justification that P_c(x) would emit small values on the other samples, assuming this alone does not tell us anything useful. The authors chose to split the new formulation to S(x)_i times P_c(x) in equation (2). If we keep the original equation and expand the cross-entropy term we get: L_i = - x_(y_i) + log(sum(exp(x_i))+exp(c)). What c does, is that it puts a lower bound on the true class activation and an upper bound on the incorrect class activation. The first term increases the activation of the true class, the second term cancels the first term once x_(y_i) >> c. The second term also decreases the other classes* activations if they are >> c and > x_(y_i)), but once they go below c they are no longer penalized. If we instead write the softmax as a hard max, it becomes more clear why this is the case. L_i = - x_(y_i) + max({x_i}+{c}) Unlike the original cross-entropy loss that squashes other probabilities to 0 and the correct class to 1, the inhibited softmax would be happy if the other probabilities are less than some threshold t > 0 (which is a function of c) and the correct class probability > t. From this perspective, this approach is just another way to penalize the predictions similar to [1] in which the authors penalize the negative entropy of the prediction to encourage a more uniform prediction. That method also has no additional learnable parameters, works with only a single forward pass, requires no more out-of-distribution data, and has no input "preprocessing." In section 3.1 the authors: - (i) Propose to remove the bias in the final layer of the neural network to eliminate data-independency. Removing the bias from the final layer should have minimal effect on the ability of network in inducing data-independent bias -- the bias of the final layer can be directly absorbed in the bias of the layer immediate before that. - (ii) the second paragraph that discusses replacing the penultimate layer with a kernel function is not clear. - (iii) The assertion that x_i increases boundlessly is wrong. Adding a constant value to all of x_i*s does not change the loss of the original cross-entropy term -- it cancels out in the probability; furthermore, the gradient of - log P_c converges to zero quickly when max(x_i) >> c. If we expand the term - log P_c to - log(sum(exp(x_i))) + log(sum(exp(x_i))+exp(c)), and then simplify it to - max(x_i) + max({x_i}+{c}), we*ll see that when x_i is sufficiently larger than c, the first term and the second term cancel out. This cancelling effect happens immediately with a hard max when max(x_i)>c, but for a softmax, it is more gradual, but quick nonetheless. Therefore the x_i*s should not be increasing boundlessly. - (iv) Applying l2 regularization only on the weights of the final layer merely pushes the high-magnitude activation to the layer immediate before it. Unless we regularize all the layers, this should have minimal effect. It seems, empirically, that the deeper the network, the more the overconfidence problem [2]. The overconfidence makes the detection of out-of-distribution samples in modern neural networks particularly more difficult. The first matter that concerns me in the evaluation of this work is that the networks under study, in addition to being non-standard, are much shallower than the common neural networks that are used for evaluation of out-of-distribution samples. For instance, (Wide)-ResNets are commonly used in previous work such as [3] to evaluate the method for OOD detection. The OOD problem in the shallow networks is a relatively easier problem than the OOD detection in deep networks. The second concern is that the set of chosen datasets for in-distribution and out-of-distribution are far from challenging: the datasets are so different that a linear classifier based on the local image statistics can separate them. Some more challenging datasets that appear in the OOD detection literature are CIFAR100, TinyImageNet, and SUN. Quality. The provided theoretical analysis are either incomplete or need a more careful review by the authors. See above. Clarity. The paper is overall easy to follow and understand. However, there are a few instances that should be clarified. The paragraph that provides intuition on why inhibited softmax serves as an uncertainty estimator could be improved. The paragraph explaining the adjustment by using a kernel activation function is not clear. The explanation of the experiments (the three bullet points in section 4) can be improved by adding more details. Specifically, the measures should be explained properly. Originality. The three main contributions are, (i) the mathematical explanation, which as discussed earlier is not clear (to me) and seems insufficient by itself. (ii) The additional adjustments, which as discussed earlier were not convincing, and (iii) the benchmarks comparing the proposed idea with some of the recent methods that I believe do not reflect a conclusive picture because of the two stated concerns. The idea of inhibited softmax itself comes from another publication (Saito et al. in the paper). I would be happy to change my rating if I have misunderstood parts of the paper or have made a mistake in my review. References. [1] G. Pereyra, G. Tucker, J. Chorowski, Ł. Kaiser, and G. Hinton, “Regularizing Neural Networks by Penalizing Confident Output Distributions,” ICLR Work., 2017 [2] C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On Calibration of Modern Neural Networks,” ICML, 2017. [3] S. Liang, Y. Li, and R. Srikant, “Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks,” ICLR, 2018.