Papers open-set classification
“open-set classification” 태그가 달린 논문 47편 · 필터 해제
Data-Driven Hierarchical Open Set Recognition
This paper presents a novel data-driven hierarchical approach to open set recognition (OSR) for robust perception in robotics and computer vision, utilizing constrained agglomerative clustering to automatically build a h…
Classification Consistencyopen-set classificationOpen Set LearningLeveraging Open-Source Large Language Models for Native Language Identification
Native Language Identification (NLI) - the task of identifying the native language (L1) of a person based on their writing in the second language (L2) - has applications in forensics, marketing, and second language acqui…
Feature EngineeringLanguage AcquisitionLanguage IdentificationMarketing+2Acoustic identification of individual animals with hierarchical contrastive learning
Acoustic identification of individual animals (AIID) is closely related to audio-based species classification but requires a finer level of detail to distinguish between individual animals within the same species. In thi…
ClassificationContrastive LearningHierarchical Multi-label ClassificationMulti-Label Classification+2FungiTastic: A multi-modal dataset and benchmark for image categorization
We introduce a new, challenging benchmark and a dataset, FungiTastic, based on fungal records continuously collected over a twenty-year span. The dataset is labeled and curated by experts and consists of about 350k multi…
ClassificationFew-Shot LearningImage CategorizationMulti-modal Classification+1Large-Scale Evaluation of Open-Set Image Classification Techniques
The goal for classification is to correctly assign labels to unseen samples. However, most methods misclassify samples with unseen labels and assign them to one of the known classes. Open-Set Classification (OSC) algorit…
image-classificationImage Classificationopen-set classificationOpen Set LearningBOSC: A Backdoor-based Framework for Open Set Synthetic Image Attribution
Synthetic image attribution addresses the problem of tracing back the origin of images produced by generative models. Extensive efforts have been made to explore unique representations of generative models and use them t…
AttributeImage Attributionopen-set classificationSynthetic Image AttributionModel Pairing Using Embedding Translation for Backdoor Attack Detection on Open-Set Classification Tasks
Backdoor attacks allow an attacker to embed a specific vulnerability in a machine learning algorithm, activated when an attacker-chosen pattern is presented, causing a specific misprediction. The need to identify backdoo…
Backdoor Attackopen-set classificationInfinite dSprites for Disentangled Continual Learning: Separating Memory Edits from Generalization
The ability of machine learning systems to learn continually is hindered by catastrophic forgetting, the tendency of neural networks to overwrite previously acquired knowledge when learning a new task. Existing methods m…
ClassificationContinual LearningDisentanglementMemorization+1Open-Set Face Recognition with Maximal Entropy and Objectosphere Loss
Open-set face recognition characterizes a scenario where unknown individuals, unseen during the training and enrollment stages, appear on operation time. This work concentrates on watchlists, an open-set task that is exp…
Domain AdaptationFace RecognitionImage Classificationopen-set classification+1Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification
The evolution of Internet and its related communication technologies have consistently increased the risk of cyber-attacks. In this context, a crucial role is played by Intrusion Detection Systems (IDSs), which are secur…
Active LearningClassificationIntrusion DetectionNetwork Intrusion Detection+3IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization
Semi-supervised learning (SSL) aims to leverage massive unlabeled data when labels are expensive to obtain. Unfortunately, in many real-world applications, the collected unlabeled data will inevitably contain unseen-clas…
open-set classificationOpen-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation
Open-set face recognition refers to a scenario in which biometric systems have incomplete knowledge of all existing subjects. Therefore, they are expected to prevent face samples of unregistered subjects from being ident…
Face RecognitionImage Classificationopen-set classificationOpen Set LearningRobust open-set classification for encrypted traffic fingerprinting
Encrypted network traffic has been known to leak information about their underlying content through side-channel information leaks. Traffic fingerprinting attacks exploit this by using machine learning techniques to thre…
Classificationopen-set classificationQuantization$\mathcal{G}^2Pxy$: Generative Open-Set Node Classification on Graphs with Proxy Unknowns
Node classification is the task of predicting the labels of unlabeled nodes in a graph. State-of-the-art methods based on graph neural networks achieve excellent performance when all labels are available during training.…
ClassificationInductive LearningNode Classificationopen-set classification+1OpenNDD: Open Set Recognition for Neurodevelopmental Disorders Detection
Since the strong comorbid similarity in NDDs, such as attention-deficit hyperactivity disorder, can interfere with the accurate diagnosis of autism spectrum disorder (ASD), identifying unknown classes is extremely crucia…
open-set classificationOpen Set LearningProTeCt: Prompt Tuning for Taxonomic Open Set Classification
Visual-language foundation models, like CLIP, learn generalized representations that enable zero-shot open-set classification. Few-shot adaptation methods, based on prompt tuning, have been shown to further improve perfo…
Classificationopen-set classificationOpen Set Classification of GAN-based Image Manipulations via a ViT-based Hybrid Architecture
Classification of AI-manipulated content is receiving great attention, for distinguishing different types of manipulations. Most of the methods developed so far fail in the open-set scenario, that is when the algorithm u…
AttributeClassificationFace Generationopen-set classificationLearning Pairwise Interaction for Generalizable DeepFake Detection
A fast-paced development of DeepFake generation techniques challenge the detection schemes designed for known type DeepFakes. A reliable Deepfake detection approach must be agnostic to generation types, which can present…
Decision MakingDeepFake DetectionFace Swappingopen-set classificationText Classification in the Wild: a Large-scale Long-tailed Name Normalization Dataset
Real-world data usually exhibits a long-tailed distribution,with a few frequent labels and a lot of few-shot labels. The study of institution name normalization is a perfect application case showing this phenomenon. Ther…
Long-tail Learningopen-set classificationOut-of-Distribution Generalizationtext-classification+2Open-Set Automatic Target Recognition
Automatic Target Recognition (ATR) is a category of computer vision algorithms which attempts to recognize targets on data obtained from different sensors. ATR algorithms are extensively used in real-world scenarios such…
open-set classificationOpen Set Learning