We exploit this relationship by using the clinical data as pseudo-labels for our data without biomarker labels in order to choose positive and negative instances for training a backbone network with a supervised contrastive loss.
In this work, we present a methodology to shape a fisheye-specific representation space that reflects the interaction between distortion and semantic context present in this data modality.
We define purview as the additional capacity necessary to characterize inference samples that differ from the training data.
To alleviate this issue, we propose a grounded second-order definition of information content and sample importance within the context of active learning.
This paper conjectures and validates a framework that allows for action during inference in supervised neural networks.
However, existing strategies directly base the data selection on the data representation of the unlabeled data which is random for OOD samples by definition.
Our evaluation of existing uncertainty estimation algorithms, with the presence of HLU, indicates the limitations of existing uncertainty metrics and algorithms themselves in response to HLU.
This is accomplished by leveraging the larger amount of clinical data as pseudo-labels for our data without biomarker labels in order to choose positive and negative instances for training a backbone network with a supervised contrastive loss.
The dataset consists of 1268 near-IR fundus images each with at least 49 OCT scans, and 16 biomarkers, along with 4 clinical labels and a disease diagnosis of DR or DME.
Finally, we ground the proposed machine introspection to human introspection for the application of image quality assessment.
In seismic interpretation, pixel-level labels of various rock structures can be time-consuming and expensive to obtain.
Neural networks trained to classify images do so by identifying features that allow them to distinguish between classes.
To articulate the significance of the model perspective in novelty detection, we utilize backpropagated gradients.
In this paper, we show that existing recognition and localization deep architectures, that have not been exposed to eye tracking data or any saliency datasets, are capable of predicting the human visual saliency.
To complement the learned information from activation-based representation, we propose utilizing a gradient-based representation that explicitly focuses on missing information.
In this paper, we utilize weight gradients from backpropagation to characterize the representation space learned by deep learning algorithms.
In this paper, we generate and control semantically interpretable filters that are directly learned from natural images in an unsupervised fashion.
We use multiple linear decoders to capture different abstraction levels of the image patches.
While assessing image quality, the filters need to capture perceptual differences based on dissimilarities between a reference image and its distorted version.
We benchmark the performance of existing solutions in real-world scenarios and analyze the performance variation with respect to challenging conditions.