Robust classification

94 papers with code • 2 benchmarks • 6 datasets

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Use these libraries to find Robust classification models and implementations
2 papers

Most implemented papers

Towards Deep Learning Models Resistant to Adversarial Attacks

MadryLab/mnist_challenge 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

locuslab/smoothing 8 Feb 2019

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 14 Nov 2019

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

xinario/catgan_pytorch 19 Nov 2015

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 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 NeurIPS 2020

We present a method for provably defending any pretrained image classifier against $\ell_p$ adversarial attacks.

SWAD: Domain Generalization by Seeking Flat Minima

khanrc/swad NeurIPS 2021

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

areylng/MemSeg 2 May 2022

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 CVPR 2018

To improve the robustness, we propose a novel learning framework called convolutional prototype learning (CPL).

Towards the first adversarially robust neural network model on MNIST

bethgelab/AnalysisBySynthesis ICLR 2019

Despite much effort, deep neural networks remain highly susceptible to tiny input perturbations and even for MNIST, one of the most common toy datasets in computer vision, no neural network model exists for which adversarial perturbations are large and make semantic sense to humans.