Robust classification
95 papers with code • 2 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Robust classification
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Libraries
Use these libraries to find Robust classification models and implementations
CIFAR-10
MNIST-C
ModelNet40-C
DeepWeeds
N-ImageNet
WHYSHIFT
Latest papers with no code
centroIDA: Cross-Domain Class Discrepancy Minimization Based on Accumulative Class-Centroids for Imbalanced Domain Adaptation
Unsupervised Domain Adaptation (UDA) approaches address the covariate shift problem by minimizing the distribution discrepancy between the source and target domains, assuming that the label distribution is invariant across domains.
FaFCNN: A General Disease Classification Framework Based on Feature Fusion Neural Networks
There are two fundamental problems in applying deep learning/machine learning methods to disease classification tasks, one is the insufficient number and poor quality of training samples; another one is how to effectively fuse multiple source features and thus train robust classification models.
Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data
In this paper, we propose a systematic methodology for developing robust models for surgical tool detection using noisy data.
Revisiting Image Classifier Training for Improved Certified Robust Defense against Adversarial Patches
The success of this strategy relies heavily on the model's invariance to image pixel masking.
Explainable AI and Machine Learning Towards Human Gait Deterioration Analysis
By linking clinically observable features to the model outputs, we demonstrate the impact of PD severity on gait.
Fourier Test-time Adaptation with Multi-level Consistency for Robust Classification
Second, we introduce a regularization technique that utilizes style interpolation consistency in the frequency space to encourage self-consistency in the logit space of the model output.
A Robust Classifier Under Missing-Not-At-Random Sample Selection Bias
In this paper, we propose BiasCorr, an algorithm that improves on Greene's method by modifying the original training set in order for a classifier to learn under MNAR sample selection bias.
Enhanced Multi-level Features for Very High Resolution Remote Sensing Scene Classification
Very high-resolution (VHR) remote sensing (RS) scene classification is a challenging task due to the higher inter-class similarity and intra-class variability problems.
On the Role of Randomization in Adversarially Robust Classification
Deep neural networks are known to be vulnerable to small adversarial perturbations in test data.
Robust 3D Shape Classification via Non-Local Graph Attention Network
Especially, in the case of sparse point clouds (64 points) with noise under arbitrary SO(3) rotation, the classification result (85. 4%) of NLGAT is improved by 39. 4% compared with the best development of other methods.
