Deep learning (DL) models have significantly transformed machine learning (ML), particularly with their prowess in classification tasks. However, these models struggle to differentiate between in-distribution (ID) and out-of-distribution (OOD) data at the testing phase. This challenge has curtailed their deployment in sensitive fields like biotechnology, where misidentifying OOD data, such as unclear or unknown bacterial genomic sequences, as known ID classes could lead to dire consequences. To address this, we propose an approach to make DL models OOD-sensitive by exploiting the configuration of the logit space embeddings, into the model’s decision-making process. Leveraging the effect observed in recent studies that there is minimal overlap between the embeddings of ID and OOD data, we use a density estimator to model the ID logit distribution based on the training data. This allows us to reliably flag data that do not match the ID distribution as OOD. Our methodology is designed to be independent of the specific data or model architecture and can seamlessly augment existing trained models without the need to expose them to OOD data. Testing our method on widely recognized image datasets, we achieve leading-edge results, including a substantial 10% enhancement in the area under the receiver operating characteristic curve (AUCROC) on the Google genome dataset.

Spectral clustering is a widely adopted method capable of identifying complicated cluster boundaries. However, traditional spectral clustering requires the definition of a predefined similarity metric for constructing the Laplacian matrix, a requirement that limits flexibility and adaptability. Instead of predefining this metric upfront as a fixed parametric function, we introduce a novel approach that learns the optimal parameters of a similarity function through parameter optimization. This optimizes a similarity function to assign high similarity values to data pairs with shared discriminative features and low values to those without such features. Previous methods that adapt similarity measures typically treat their parameters as hyperparameters or rely on non-convex optimization strategies. However, these approaches are not well-suited for unsupervised scenarios, as they depend heavily on initial conditions and require labeled data for validation, which is unavailable in such settings. In contrast, our method employs convex optimization to learn the parameters of the similarity metrics directly, rather than treating them as hyperparameters. This enables robust and reliable unsupervised learning, making our approach particularly well-suited for spectral clustering. We validate the effectiveness and adaptability of our method on several benchmark datasets, demonstrating superior performance compared to existing techniques.

As the research community focuses on improving the reliability of deep learning, identifying out-of-distribution (OOD) data has become crucial. Detecting OOD inputs during test/prediction allows the model to account for discriminative features unknown to the model. This capability increases the model's reliability since this model provides a class prediction solely at incoming data similar to the training one. Although OOD detection is well-established in computer vision, it is relatively unexplored in other areas, like time series-based human activity recognition (HAR). Since uncertainty has been a critical driver for OOD in vision-based models, the same component has proven effective in time-series applications. In this work, we propose an ensemble-based temporal learning framework to address the OOD detection problem in HAR with time-series data. First, we define different types of OOD for HAR that arise from realistic scenarios. Then we apply our ensemble-based temporal learning framework incorporating uncertainty to detect OODs for the defined HAR workloads. This particular formulation also allows a novel approach to fall detection. We train our model on non-fall activities and detect falls as OOD. Our method shows state-of-The-Art performance in a fall detection task using much lesser data. Furthermore, the ensemble framework outperformed the traditional deep-learning method (our baseline) on the OOD detection task across all the other chosen datasets.

With the growing interest of the research community in making deep learning (DL) robust and reliable, detecting out-of-distribution (OOD) data has become critical. Detecting OOD inputs during test/prediction allows the model to account for discriminative features unknown to the model. This capability increases the model's reliability since this model provides a class prediction solely at incoming data similar to the training one. OOD detection is well established in computer vision problems. However, it remains relatively under-explored in other domains such as time series (i.e., Human Activity Recognition (HAR)). Since uncertainty has been a critical driver for OOD in vision-based models, the same component has proven effective in time-series applications. We plan to address the OOD detection problem in HAR with time-series data in this work. To test the capability of the proposed method, we define different types of OOD for HAR that arise from realistic scenarios. We apply an ensemble-based temporal learning framework that incorporates uncertainty and detects OOD for the defined HAR workloads. In particular, we extract OODs from popular benchmark HAR datasets and use the framework to separate those OODs from the indistribution (ID) data. Across all the datasets, the ensemble framework outperformed the traditional deep-learning method (our baseline) on the OOD detection task.