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Found 5 papers, 0 papers with code
DiffCAM: Data-Driven Saliency Maps by Capturing Feature Differences
In recent years, the interpretability of Deep Neural Networks (DNNs) has garnered significant attention, particularly due to their widespread deployment in critical domains like healthcare, finance, and autonomous systems.
Improving Knowledge Distillation in Transfer Learning with Layer-wise Learning Rates
Contrary to these methods, in this work, we propose a novel layer-wise learning scheme that adjusts learning parameters per layer as a function of the differences in the Jacobian/Attention/Hessian of the output activations w. r. t.
DualContrast: Unsupervised Disentangling of Content and Transformations with Implicit Parameterization
We demonstrate that the existing self-supervised methods with data augmentation result in the poor disentanglement of content and transformations in real-world scenarios.
Harmony: A Generic Unsupervised Approach for Disentangling Semantic Content From Parameterized Transformations
Harmony leverages a simple cross-contrastive learning framework with multiple explicitly parameterized latent representations to disentangle content from transformations.
Cryo-shift: Reducing domain shift in cryo-electron subtomograms with unsupervised domain adaptation and randomization
Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that enables the visualization of subcellular structures in situ at near-atomic resolution.
