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Found 14 papers, 6 papers with code
Neighbor-Aware Calibration of Segmentation Networks with Penalty-Based Constraints
Ensuring reliable confidence scores from deep neural networks is of paramount significance in critical decision-making systems, particularly in real-world domains such as healthcare.
Trust your neighbours: Penalty-based constraints for model calibration
Ensuring reliable confidence scores from deep networks is of pivotal importance in critical decision-making systems, notably in the medical domain.
Class Adaptive Network Calibration
Comprehensive evaluation and multiple comparisons on a variety of benchmarks, including standard and long-tailed image classification, semantic segmentation, and text classification, demonstrate the superiority of the proposed method.
Calibrating Segmentation Networks with Margin-based Label Smoothing
Following our observations, we propose a simple and flexible generalization based on inequality constraints, which imposes a controllable margin on logit distances.
GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges
However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.
The Devil is in the Margin: Margin-based Label Smoothing for Network Calibration
Following our observations, we propose a simple and flexible generalization based on inequality constraints, which imposes a controllable margin on logit distances.
Robust High-Dimensional Regression with Coefficient Thresholding and its Application to Imaging Data Analysis
It is of importance to develop statistical techniques to analyze high-dimensional data in the presence of both complex dependence and possible outliers in real-world applications such as imaging data analyses.
Segmentation with mixed supervision: Confidence maximization helps knowledge distillation
Combined with a standard cross-entropy loss over the labeled pixels, our novel formulation integrates two important terms: (i) a Shannon entropy loss defined over the less-supervised images, which encourages confident student predictions in the bottom branch; and (ii) a KL divergence term, which transfers the knowledge (i. e., predictions) of the strongly supervised branch to the less-supervised branch and guides the entropy (student-confidence) term to avoid trivial solutions.
Do We Really Need Dice? The Hidden Region-Size Biases of Segmentation Losses
Most segmentation losses are arguably variants of the Cross-Entropy (CE) or Dice losses.
Improving Neural Network Robustness through Neighborhood Preserving Layers
Finally, we empirically show that our designed network architecture is more robust against state-of-art gradient descent based attacks, such as a PGD attack on the benchmark datasets MNIST and CIFAR10.
Analyzing Attention Mechanisms through Lens of Sample Complexity and Loss Landscape
Attention mechanisms have advanced state-of-the-art deep learning models in many machine learning tasks.
A Manifold Proximal Linear Method for Sparse Spectral Clustering with Application to Single-Cell RNA Sequencing Data Analysis
This paper considers a widely adopted model for SSC, which can be formulated as an optimization problem over the Stiefel manifold with nonsmooth and nonconvex objective.
Furnishing Your Room by What You See: An End-to-End Furniture Set Retrieval Framework with Rich Annotated Benchmark Dataset
However, few works focus on the understanding of furniture within the scenes and a large-scale dataset is also lacked to advance the field.
Understanding Attention Mechanisms
Attention mechanisms have advanced the state of the art in several machine learning tasks.
