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
Most implemented papers
Label-Noise Robust Generative Adversarial Networks
To remedy this, we propose a novel family of GANs called label-noise robust GANs (rGANs), which, by incorporating a noise transition model, can learn a clean label conditional generative distribution even when training labels are noisy.
Implicit Generation and Generalization in Energy-Based Models
Energy based models (EBMs) are appealing due to their generality and simplicity in likelihood modeling, but have been traditionally difficult to train.
Masksembles for Uncertainty Estimation
nikitadurasov/masksembles
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CVPR 2021
Our central intuition is that there is a continuous spectrum of ensemble-like models of which MC-Dropout and Deep Ensembles are extreme examples.
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels
To highlight, RP with a CNN classifier can predict if an MNIST digit is a "one"or "not" with only 0. 25% error, and 0. 46 error across all digits, even when 50% of positive examples are mislabeled and 50% of observed positive labels are mislabeled negative examples.
Imbalanced Image Classification with Complement Cross Entropy
frgfm/Holocron
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Recently, deep learning models have achieved great success in computer vision applications, relying on large-scale class-balanced datasets.
Analysis of classifiers robust to noisy labels
We explore contemporary robust classification algorithms for overcoming class-dependant labelling noise: Forward, Importance Re-weighting and T-revision.
Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off
RobustBench/robustbench
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ICMLW 2021
While adversarial training has become the de facto approach for training robust classifiers, it leads to a drop in accuracy.
Robust Classification via a Single Diffusion Model
huanranchen/AdversarialAttacks
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Since our method does not require training on particular adversarial attacks, we demonstrate that it is more generalizable to defend against multiple unseen threats.
Robustifying Point Cloud Networks by Refocusing
In this study, we develop a general mechanism to increase neural network robustness based on focus analysis.
Polynomial expansion of the binary classification function
This paper describes a novel method to approximate the polynomial coefficients of regression functions, with particular interest on multi-dimensional classification.
