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Found 4 papers, 2 papers with code
ADFA: Attention-augmented Differentiable top-k Feature Adaptation for Unsupervised Medical Anomaly Detection
We then apply differentiable top-k feature adaptation to train the patch descriptor, mapping the extracted feature representations to a new vector space, enabling effective detection of anomalies.
Elucidating Meta-Structures of Noisy Labels in Semantic Segmentation by Deep Neural Networks
Our understanding of the learning behavior of DNNs trained by noisy segmentation labels remains limited.
Advancing biological super-resolution microscopy through deep learning: a brief review
We conclude with an outlook on how deep learning could shape the future of this new generation of light microscopy technology.
Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation
When trained with these noisy labels, DNNs provide largely the same segmentation performance as trained by the original ground truth.
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