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Found 6 papers, 1 papers with code
MaskChanger: A Transformer-Based Model Tailoring Change Detection with Mask Classification
Change detection in multi-temporal remote sensing data enables crucial urban analysis and environmental monitoring applications.
Building change detection for remote sensing images
Change Detection
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Spuriosity Rankings for Free: A Simple Framework for Last Layer Retraining Based on Object Detection
A promising solution to this problem is last-layer retraining, which involves retraining the linear classifier head on a small subset of data without spurious cues.
Blacksmith: Fast Adversarial Training of Vision Transformers via a Mixture of Single-step and Multi-step Methods
Despite the remarkable success achieved by deep learning algorithms in various domains, such as computer vision, they remain vulnerable to adversarial perturbations.
Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework
Despite considerable theoretical progress in the training of neural networks viewed as a multi-agent system of neurons, particularly concerning biological plausibility and decentralized training, their applicability to real-world problems remains limited due to scalability issues.
A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection
For this purpose, we first demonstrate that the existing model-based methods can be equivalent to applying smaller perturbation or optimization weights to the hard training examples.
Your Out-of-Distribution Detection Method is Not Robust!
Therefore, unlike OOD detection in the standard setting, access to OOD, as well as in-distribution, samples sounds necessary in the adversarial training setup.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
