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 with no code
Coordinated Sparse Recovery of Label Noise
Based on CSR, this study designs a joint sample selection strategy and constructs a comprehensive and powerful learning framework called CSR+.
FakeWatch: A Framework for Detecting Fake News to Ensure Credible Elections
In today's technologically driven world, the rapid spread of fake news, particularly during critical events like elections, poses a growing threat to the integrity of information.
Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification
In this paper, we introduce an Iterative Online Image Synthesis (IOIS) framework to address the class imbalance problem in medical image classification.
epsilon-Mesh Attack: A Surface-based Adversarial Point Cloud Attack for Facial Expression Recognition
Due to the wide application area of point clouds and the recent advancements in deep neural networks, studies focusing on robust classification of the 3D point cloud data emerged.
Ransomware detection using stacked autoencoder for feature selection
The aim of this study is to propose and evaluate an advanced ransomware detection and classification method that combines a Stacked Autoencoder (SAE) for precise feature selection with a Long Short Term Memory (LSTM) classifier to enhance ransomware stratification accuracy.
TriAug: Out-of-Distribution Detection for Imbalanced Breast Lesion in Ultrasound
Different diseases, such as histological subtypes of breast lesions, have severely varying incidence rates.
Exploiting GPT-4 Vision for Zero-shot Point Cloud Understanding
In this study, we tackle the challenge of classifying the object category in point clouds, which previous works like PointCLIP struggle to address due to the inherent limitations of the CLIP architecture.
Learning Optimal Classification Trees Robust to Distribution Shifts
We consider the problem of learning classification trees that are robust to distribution shifts between training and testing/deployment data.
VECHR: A Dataset for Explainable and Robust Classification of Vulnerability Type in the European Court of Human Rights
Our results demonstrate the challenging nature of the task with lower prediction performance and limited agreement between models and experts.
Extended target tracking utilizing machine-learning software -- with applications to animal classification
This paper considers the problem of detecting and tracking objects in a sequence of images.
