Robust classification
116 papers with code • 2 benchmarks • 10 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 implementationsDatasets
CIFAR-10
MNIST-C
ModelNet40-C
DeepWeeds
N-ImageNet
MedMNIST-C
WHYSHIFT
Tiny ImageNet-R
EuroSAT-C
Tiny ImageNet-A
Most implemented papers
Towards Deep Learning Models Resistant to Adversarial Attacks
MadryLab/mnist_challenge
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ICLR 2018
Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal.
Certified Adversarial Robustness via Randomized Smoothing
We show how to turn any classifier that classifies well under Gaussian noise into a new classifier that is certifiably robust to adversarial perturbations under the $\ell_2$ norm.
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
MegviiDetection/video_analyst
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Following these guidelines, we design our Fully Convolutional Siamese tracker++ (SiamFC++) by introducing both classification and target state estimation branch(G1), classification score without ambiguity(G2), tracking without prior knowledge(G3), and estimation quality score(G4).
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
Our approach is based on an objective function that trades-off mutual information between observed examples and their predicted categorical class distribution, against robustness of the classifier to an adversarial generative model.
Unlabeled Data Improves Adversarial Robustness
yaircarmon/semisup-adv
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NeurIPS 2019
We demonstrate, theoretically and empirically, that adversarial robustness can significantly benefit from semisupervised learning.
Denoised Smoothing: A Provable Defense for Pretrained Classifiers
microsoft/blackbox-smoothing
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NeurIPS 2020
We present a method for provably defending any pretrained image classifier against $\ell_p$ adversarial attacks.
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.
SWAD: Domain Generalization by Seeking Flat Minima
Domain generalization (DG) methods aim to achieve generalizability to an unseen target domain by using only training data from the source domains.
MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities
TooTouch/MemSeg
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By comparing the similarities and differences between input samples and memory samples in the memory pool to give effective guesses about abnormal regions; In the inference phase, MemSeg directly determines the abnormal regions of the input image in an end-to-end manner.
Robust Classification with Convolutional Prototype Learning
YangHM/Convolutional-Prototype-Learning
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CVPR 2018
To improve the robustness, we propose a novel learning framework called convolutional prototype learning (CPL).
