Robust classification
94 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
CBGT-Net: A Neuromimetic Architecture for Robust Classification of Streaming Data
This paper describes CBGT-Net, a neural network model inspired by the cortico-basal ganglia-thalamic (CBGT) circuits found in mammalian brains.
Your Diffusion Model is Secretly a Certifiably Robust Classifier
huanranchen/NoisedDiffusionClassifiers
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Diffusion models are recently employed as generative classifiers for robust classification.
MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers
Bai-YT/MixedNUTS
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Adversarial robustness often comes at the cost of degraded accuracy, impeding the real-life application of robust classification models.
Decoding Envelope and Frequency-Following EEG Responses to Continuous Speech Using Deep Neural Networks
mike-boop/match-mismatch-decoders-ojsp-2023
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The electroencephalogram (EEG) offers a non-invasive means by which a listener's auditory system may be monitored during continuous speech perception.
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy
Modern computer vision offers a great variety of models to practitioners, and selecting a model from multiple options for specific applications can be challenging.
Robust-GBDT: GBDT with Nonconvex Loss for Tabular Classification in the Presence of Label Noise and Class Imbalance
This research heralds a paradigm shift in machine learning, paving the way for a new era of robust and precise classification across diverse real-world applications.
Robustifying Point Cloud Networks by Refocusing
yossilevii100/critical_points2
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In this study, we develop a general mechanism to increase neural network robustness based on focus analysis.
Distributionally Robust Classification on a Data Budget
penfever/vlhub
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To our knowledge, this is the first result showing (near) state-of-the-art distributional robustness on limited data budgets.
Computational Asymmetries in Robust Classification
samuelemarro/counter-attack
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In the context of adversarial robustness, we make three strongly related contributions.
Robust Classification of High-Dimensional Data using Data-Adaptive Energy Distance
Classification of high-dimensional low sample size (HDLSS) data poses a challenge in a variety of real-world situations, such as gene expression studies, cancer research, and medical imaging.
