This paper presents a novel approach to active learning that takes into
...
This paper presents a novel positive and negative set selection strategy...
In this work, we present a methodology to shape a fisheye-specific
repre...
We analyze the data-dependent capacity of neural networks and assess
ano...
This paper conjectures and validates a framework that allows for action
...
This paper considers deep out-of-distribution active learning. In practi...
Humans exhibit disagreement during data labeling. We term this disagreem...
This paper presents a novel positive and negative set selection strategy...
Clinical diagnosis of the eye is performed over multifarious data modali...
In this paper, we advocate for two stages in a neural network's decision...
We propose to utilize gradients for detecting adversarial and
out-of-dis...
In seismic interpretation, pixel-level labels of various rock structures...
In this article, we present a leap-forward expansion to the study of
exp...
Neural networks represent data as projections on trained weights in a hi...
Neural networks trained to classify images do so by identifying features...
In this paper, we propose a model-based characterization of neural netwo...
In this paper, we show that existing recognition and localization deep
a...
Visual explanations are logical arguments based on visual features that
...
Learning representations that clearly distinguish between normal and abn...
In this paper, we utilize weight gradients from backpropagation to
chara...
In this paper, we generate and control semantically interpretable filter...
In this paper, we train independent linear decoder models to estimate th...
In this paper, we introduce an adaptive unsupervised learning framework,...
In this paper, we investigate the robustness of traffic sign recognition...