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Found 6 papers, 3 papers with code
Improving Shift Invariance in Convolutional Neural Networks with Translation Invariant Polyphase Sampling
Downsampling operators break the shift invariance of convolutional neural networks (CNNs) and this affects the robustness of features learned by CNNs when dealing with even small pixel-level shift.
SeeBel: Seeing is Believing
While the significance of discovering and introspecting the correlation between dataset statistics and AI model performance for dense prediction computer vision tasks such as semantic segmentation is well established in the computer vision literature, to the best of our knowledge, no visualization tools have been proposed to view and analyze the aforementioned tasks.
RFC-Net: Learning High Resolution Global Features for Medical Image Segmentation on a Computational Budget
Learning High-Resolution representations is essential for semantic segmentation.
Mitigating domain shift in AI-based tuberculosis screening with unsupervised domain adaptation
We show that without domain adaptation, ResNet-50 has difficulty in generalizing between imaging distributions from a number of public Tuberculosis screening datasets with imagery from geographically distributed regions.
Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
Diagnosing different retinal diseases from Spectral Domain Optical Coherence Tomography (SD-OCT) images is a challenging task.
Ranked #1 on
Retinal OCT Disease Classification
on Srinivasan2014
Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks
Recognizing Traffic Signs using intelligent systems can drastically reduce the number of accidents happening world-wide.
