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Found 13 papers, 3 papers with code
Efficient Representation of the Activation Space in Deep Neural Networks
The representations of the activation space of deep neural networks (DNNs) are widely utilized for tasks like natural language processing, anomaly detection and speech recognition.
Weakly Supervised Detection of Hallucinations in LLM Activations
We introduce a weakly supervised auditing technique using a subset scanning approach to detect anomalous patterns in LLM activations from pre-trained models.
Model-free feature selection to facilitate automatic discovery of divergent subgroups in tabular data
Existing feature selection techniques for tabular data often involve fitting a particular model in order to select important features.
Towards Creativity Characterization of Generative Models via Group-based Subset Scanning
We propose group-based subset scanning to identify, quantify, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of the generative models.
Sparsity-based Feature Selection for Anomalous Subgroup Discovery
However, there is a common lack of a principled and scalable feature selection method for efficient discovery.
Pattern Detection in the Activation Space for Identifying Synthesized Content
Generative Adversarial Networks (GANs) have recently achieved unprecedented success in photo-realistic image synthesis from low-dimensional random noise.
Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanning
Current skin disease models could make incorrect inferences for test samples from different hardware devices and clinical settings or unknown disease samples, which are out-of-distribution (OOD) from the training samples.
Towards creativity characterization of generative models via group-based subset scanning
We propose group-based subset scanning to quantify, detect, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of generative models.
Prediction of neonatal mortality in Sub-Saharan African countries using data-level linkage of multiple surveys
Existing datasets available to address crucial problems, such as child mortality and family planning discontinuation in developing countries, are not ample for data-driven approaches.
Identifying Audio Adversarial Examples via Anomalous Pattern Detection
Audio processing models based on deep neural networks are susceptible to adversarial attacks even when the adversarial audio waveform is 99. 9% similar to a benign sample.
Deep Mining: Detecting Anomalous Patterns in Neural Network Activations with Subset Scanning
This work introduces ``Subset Scanning methods from the anomalous pattern detection domain to the task of detecting anomalous inputs to neural networks.
Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models
With the intention to create an enhanced visual explanation in terms of visual sharpness, object localization and explaining multiple occurrences of objects in a single image, we present Smooth Grad-CAM++ \footnote{Simple demo: http://35. 238. 22. 135:5000/}, a technique that combines methods from two other recent techniques---SMOOTHGRAD and Grad-CAM++.
Subset Scanning Over Neural Network Activations
This is the first work to introduce "Subset Scanning" methods from the anomalous pattern detection domain to the task of detecting anomalous input of neural networks.
