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Found 4 papers, 1 papers with code
A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes
While datasets with single-label supervision have propelled rapid advances in image classification, additional annotations are necessary in order to quantitatively assess how models make predictions.
The Intrinsic Dimension of Images and Its Impact on Learning
We find that common natural image datasets indeed have very low intrinsic dimension relative to the high number of pixels in the images.
Adversarial Robustness of Flow-Based Generative Models
Finally, using a hybrid adversarial training procedure, we significantly boost the robustness of these generative models.
Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks
Functional groups (FGs) are molecular substructures that are served as a foundation for analyzing and predicting chemical properties of molecules.
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