TopoMap: A Feature-based Discriminator of the Topographical Regions in the Test Input Space

summary

Video file (mp4)

The gist

The gist Testing Deep Learning (DL)-based systems is an open challenge, and this paper introduces TOPOMAP, an explicit topographical map of the input feature space designed to explore all regions

In short

TopoMap is a model-agnostic method that creates a topographical map of input feature space to find regions where Deep Learning models are likely to fail. It works by extracting input features, reducing their dimensionality, and clustering them. A Deep Neural Network is then used to automatically select the best combination of feature reduction and clustering techniques for creating meaningful failure regions.

Key concepts

Input Embedding Computation
This step converts high-dimensional input data into a lower-dimensional representation (embedding). The goal is to keep the essential features that cause a specific model behavior while simplifying the data structure. Various techniques like PCA, UMAP, and Autoencoders are tested to find embeddings that best capture these critical input characteristics.
Input Data Clustering
Once embedded, inputs are grouped into clusters using unsupervised learning algorithms such as K-means or HDBSCAN. This process groups inputs based on shared features within the reduced space. These resulting clusters represent distinct regions in the input feature space, potentially indicating different behaviors for the DL model.
Optimal Map Selection
A Deep Neural Network (DNN) acts as an automated evaluator to determine which combination of embedding and clustering methods creates the most useful map. The DNN classifies pairs of clusters based on their features, aiming to select the configuration that best separates inputs into discriminative groups for fault detection.
Mutation Analysis
This evaluation method tests how well the topographical map helps in selecting 'mutation-killing' inputs—inputs that cause a model to fail when slightly altered. The effectiveness of TopoMap is measured by its ability to outperform random selection in identifying these critical failure regions.

Terminology used across episodes

This episode discusses

The paper

TopoMap: A Feature-based Discriminator of the Topographical Regions in the Test Input Space · Read on arXiv

Gianmarco De Vita, Nargiz Humbatova, Paolo Tonella

Università della Svizzera italiana

Testing Deep Learning (DL)-based systems is an open challenge. Although it is relatively easy to find inputs that cause a DL model to misbehave, the grouping of such inputs by features that make them problematic for the DL model under test is largely unexplored. In this work, we propose TopoMap, a model-agnostic approach that relies solely on features that characterise inputs to construct a partitioning of the input space where each region represents a specific set of distinguishable input features. Such a partitioning gives users a possibility to identify and analyse areas that are under-represented, show higher failure rates, or exhibit prediction changes after a model update. The identified regions of interest can then be used for additional data generation or can help prioritise already generated inputs that require labelling efforts. To discriminate inputs according to the specific features they share, we first apply dimensionality reduction to obtain input embeddings that are then subjected to clustering. Each DL model might require specific embedding computations and clustering algorithms, and specific configurations of the two, to achieve a meaningful separation of inputs into discriminative groups. We propose a novel way to evaluate alternative configurations of embedding and clustering techniques. We use a deep neural network (DNN) as an approximation of a human evaluator who could tell whether a pair of clusters can be discriminated based on the features of the included elements. The evaluation results show that the maps generated by TopoMap consist of distinguishable and meaningful regions. In addition, we evaluate the effectiveness of TopoMap using mutation analysis. In particular, we assess whether the clusters in our topographical map allow for an effective selection of mutation-killing inputs.

Transcript

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Hi, I'm Tom.

Jane: And I'm Jane, here with Lu, Meng and our in-house large language model, Lalam.

Tom: We're looking at the paper: "TopoMap: A Feature-based Discriminator of the Topographical Regions in the Test Input Space".

Jane: Here's the gist of the paper. The gist Testing Deep Learning (DL)-based systems is an open challenge, and this paper introduces TOPOMAP, an explicit topographical map of the input feature space designed to explore all regions that may potentially trigger a misbehaviour of a DL model<ref:2509.03242#pg2>.

Tom: How it works TOPOMAP is described as a black-box and model-agnostic approach that relies solely on features that characterise the input space<ref:2509.03242#pg2>.

Jane: The process involves several key steps to create the topographical map<ref:2509.03242#pg7>.

Paper summary: Tom: Moving on, so to summarize this paper, TopoMap is proposed as a novel approach for fully automated creation of a topographical map of inputs. It groups inputs into regions based on their features.

Jane: The thesis is that this mapping can explore all regions that might potentially trigger a misbehavior of the DL model. It claims it’s model-agnostic because it relies only on features that characterize the input space, not the internal workings of the DL system under test.

Lu: Essentially, they are taking complex inputs and creating an input embedding with lower dimensionality while keeping all the key features that cause a specific behavior in the system under test.

Meng: This embedding step is crucial because it handles that high dimensionality of data being processed, which is something you see in deep learning systems.

Tom: After getting those embeddings, they use unsupervised learning methods to group inputs into clusters based on the characteristics of that lower-dimensional data. They consider algorithms like K-means and HDBSCAN for this grouping.

Jane: The paper then introduces a way to automate the selection of the best set of clusters by using a deep neural network as an approximation of a human evaluator to classify pairs of clusters based on their features.

Lu: This DNN is trained on pseudo-labels produced by the clustering, and its goal is to select that specific clustering configuration that yields the highest prediction accuracy on their test set.

Meng: It’s a way to automate the search for optimal embedding and clustering techniques without needing someone to manually configure everything perfectly.

Tom: The paper then evaluates this by checking if these maps are actually useful, and they use mutation analysis to assess whether the clusters allow for an effective selection of mutation-killing inputs.

Jane: The results show that their approach is very effective at locating input space regions with a high density of inputs sharing features that expose faults in DNN models.

Lu: They found that this method can identify mutation killing inputs very efficiently, achieving a killing probability of one hundred percent with just nine percent of the inputs, which is much better than random selection <ref:2509.03242#pg3,can identify mutation killing inputs very efficiently, achieving a killing probability of>.

Meng: And they showed that their approach outperforms random selection by thirty-five percent on average on killable mutants and by sixty-one percent on non-killable ones when using just nine percent of the input data.

Tom: So, the main takeaway here is that TopoMap provides a way to visualize the feature space so you can identify areas for targeted testing.

Jane: It’s about creating an explicit topographical map of inputs where each region represents a specific set of distinguishable input features. That's what we call TopoMap.

Lu: The paper is organized into sections that illustrate advances in related fields, then background on embedding computation and clustering, followed by their approach, its empirical assessment, and finally the results analysis section.

Meng: It sets up a structure for how people can build this kind of system without needing to start from scratch every time.

Tom: The next part is where we talk about the specific steps they take to actually build this topographical map.

Conclusion: Jane: So, wrapping up, when we look at "TopoMap: A Feature-based Discriminator of the Topographical Regions in the Test Input Space," it’s really about creating a topographical map of the feature space for deep learning systems.

Tom: The authors are Gianmarco De Vita, Nargiz Humbatova, and Paolo Tonella from Università della Svizzera italiana Lugano, Switzerland. This is where we put the names to the work.

Lu: The implication is that this research could serve as a first stepping stone for a model-agnostic segmentation of the input space and identifying critical regions where to locate new test input instances that have a high chance of exposing deficiencies in the DL model under test.

Meng: So, in simple terms, it’s about using features to find where the inputs are most likely to expose faults.

Jane: Precisely. It’s about using features to find where the inputs are most likely to expose faults because they share those problematic features in a DL model under test.

Tom: So, in short, TopoMap gives us a map of the input feature space so we can identify areas that might trigger misbehavior.

Lu: It’s about using features to find where inputs are most likely to expose faults because they share those problematic features in a DL model under test.

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