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Found 7 papers, 2 papers with code
Key Feature Replacement of In-Distribution Samples for Out-of-Distribution Detection
We first deduce that OOD images are perceived by a deep neural network to be semantically similar to in-distribution samples when they share a common background, as deep networks are observed to incorrectly classify such images with high confidence.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity
Although unlabeled data is readily available in pool-based AL, AL algorithms are usually evaluated by measuring the increase in supervised learning (SL) performance at consecutive acquisition steps.
Better Optimization can Reduce Sample Complexity: Active Semi-Supervised Learning via Convergence Rate Control
We name this AL scheme convergence rate control (CRC), and our experiments show that a deep neural network trained using a combination of CRC and a recently proposed SSL algorithm can quickly achieve high performance using far less labeled samples than SL.
Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation
We propose the resource-optimized neural architecture search method which can be applied to 3D medical segmentation tasks in a short training time (1. 39 days for 1GB dataset) using a small amount of computation power (one RTX 2080Ti, 10. 8GB GPU memory).
Integrating Reinforcement Learning to Self Training for Pulmonary Nodule Segmentation in Chest X-rays
Machine learning applications in medical imaging are frequently limited by the lack of quality labeled data.
Classification of Findings with Localized Lesions in Fundoscopic Images using a Regionally Guided CNN
Fundoscopic images are often investigated by ophthalmologists to spot abnormal lesions to make diagnoses.
Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks
Retinal vessel segmentation is an indispensable step for automatic detection of retinal diseases with fundoscopic images.
