user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Auto-Encoding Explanatory Examples

Paper content:
In this paper, we ask for the main factors that determine a classifier's decision making and uncover such factors by studying latent codes produced by auto-encoding frameworks.
To deliver an explanation of a classifier's behaviour, we propose a method that provides series of examples highlighting semantic differences between the classifier's decisions.
We generate these examples through interpolations in latent space.
We introduce and formalize the notion of a semantic stochastic path, as a suitable stochastic process defined in feature space via latent code interpolations.
We then introduce the concept of semantic Lagrangians as a way to incorporate the desired classifier's behaviour and find that the solution of the associated variational problem allows for highlighting differences in the classifier decision.
Very importantly, within our framework the classifier is used as a black-box, and only its evaluation is required.
A considerable drawback of the deep classification paradigm is its inability to provide explanations as to why a particular model arrives at a decision.
This black-box nature of deep systems is one of the main reasons why practitioners often hesitate to incorporate deep learning solutions in application areas, where legal or regulatory requirements demand decision-making processes to be transparent.
A state-of-the-art approach to explain misclassification is saliency maps, which can reveal the sensitivity of a classifier to its inputs.
Recent work (Adebayo et al., 2018) , however, indicates that such methods can be misleading since their results are at times independent of the model, and therefore do not provide explanations for its decisions.
The failure to correctly provide explanations by some of these methods lies in their sensibility to feature space changes, i.e. saliency maps do not leverage higher semantic representations of the data.
This motivates us to provide explanations that exploit the semantic content of the data and its relationship with the classifier.
Thus we are concerned with the question: can one find semantic differences which characterize a classifier's decision?
In this work we propose a formalism that differs from saliency maps.
Instead of characterizing particular data points, we aim at generating a set of examples which highlight differences in the decision of a black-box model.
Let us consider the task of image classification and assume a misclassification has taken place.
Imagine, for example, that a female individual was mistakenly classified as male, or a smiling face was classified as not smiling.
Our main idea is to articulate explanations for such misclassifications through sets of semantically-connected examples which link the misclassified image with a correctly classified one.
In other words, starting with the misclassified point, we change its features in a suitable way until we arrive at the correctly classified image.
Tracking the black-box output probability while changing these features can help articulate the reasons why the misclassification happened in the first place.
Now, how does one generate such a set of semantically-connected examples?
Here we propose a solution based on a variational auto-encoder framework.
We use interpolations in latent space to generate a set of examples in feature space connecting the misclassified and the correctly classified points.
We then condition the resulting feature-space paths on the black-box classifier's decisions via a user-defined functional.
Optimizing the latter over the space of paths allows us to find paths which highlight classification differences, e.g. paths along which the classifier's decision changes only once and as fast as possible.
A basic outline of our approach is given in Fig. 1 .
In what follows we introduce and formalize the notion of stochastic semantic paths -stochastic processes on feature (data) space created by decoding latent code interpolations.
We formulate the corresponding path integral formalism which allows for a Lagrangian formulation of the problem, viz. how to condition stochastic semantic paths on the output Figure 1: Auto-Encoding Examples Setup: Given a misclassified point x 0 and representatives x −T , x T , we construct suitable interpolations (stochastic processes) by means of an Auto-Encoder.
Sampling points along the interpolations produces a set of examples highlighting the classifier's decision making.
probabilities of black-box models, and introduce an example Lagrangian which tracks the classifier's decision along the paths.
We show the explanatory power of our approach on the MNIST and CelebA datasets.
In the present work we provide a novel framework to explain black-box classifiers through examples obtained from deep generative models.
To summarize, our formalism extends the auto-encoder framework by focusing on the interpolation paths in feature space.
We train the auto-encoder, not only by guaranteeing reconstruction quality, but by imposing conditions on its interpolations.
These conditions are such that information about the classification decisions of the model B is encoded in the example paths.
Beyond the specific problem of generating explanatory examples, our work formalizes the notion of a stochastic process induced in feature space by latent code interpolations, as well as quantitative characterization of the interpolation through the semantic Lagrangian's and actions.
Our methodology is not constrained to a specific Auto-Encoder framework provided that mild regularity conditions are guaranteed for the auto-encoder.
There was no preprocessing on the 28x28 MNIST images.
The models were trained with up to 100 epochs with mini-batches of size 32 -we remark that in most cases, however, acceptable convergence occurs much faster, e.g. requiring up to 15 epochs of training.
Our choice of optimizer is Adam with learning rate α = 10 −3 .
The weight of the KL term of the VAE is λ kl = 1, the path loss weight is λ p = 10 3 and the edge loss weight is λ e = 10 −1 .
We estimate the path and edge loss during training by sampling 5 paths, each of those has 20 steps.
Encoder Architecture
Both the encoder and decoder used fully convolutional architectures with 3x3 convolutional filters with stride 2.
Conv k denotes the convolution with k filters, FSConv k the fractional strides convolution with k filters (the first two of them doubling the resolution, the third one keeping it constant), BN denotes batch normalization, and as above ReLU the rectified linear units, FC k the fully connected layer to R k .
The pre-processing of the CelebA images was done by first taking a 140x140 center crop and then resizing the image to 64x64.
The models are trained with up to 100 epochs and with mini-batches of size 128.
Our choice of optimizer is Adam with learning rate α = 10 −3 .
The weight of the KL term of the VAE is λ kl = 0.5, the path loss weight is λ p = 0.5 and the edge loss weight is λ e = 10 − 3.
We estimate the path and edge loss during training by sampling 10 paths, each of those has 10 steps.
Encoder Architecture
Decoder Architecture
Both the encoder and decoder used fully convolutional architectures with 3x3 convolutional filters with stride 2.
Conv k denotes the convolution with k filters, FSConv k the fractional strides convolution with k filters (the first two of them doubling the resolution, the third one keeping it constant), BN denotes batch normalization, and as above ReLU the rectified linear units, FC k the fully connected layer to R k .
C FURTHER RESULTS
Interpolation between 2 and 7.
It is seen that the Path-VAE interpolation optimizes both probabilities (P(2) and P (7)) according to the chosen Lagrangian -in this case the minimum hesitant L 1 .
Briefly put, the construction we utilize makes use of the well-known notion of consistent measures, which are finite-dimensional projections that enjoy certain restriction compatibility; afterwards, we show existence by employing the central extension result of Kolmogorov-Daniell.

assistant: text: We generate examples to explain a classifier desicion via interpolations in latent space. The variational auto encoder cost is extended with a functional of the classifier over the generated example path in data space.