Survey of open set recognition techniques and their limitations.
problem Recognition tasks with unknown classes during testing.
method Comprehensive review of techniques, datasets, and evaluation criteria.
result Highlighting the limitations and future directions in open set recognition.
Paper tackles dynamic open world recognition in online settings.
problem Dynamic open world recognition in online settings.
method Incremental learning of the underlying metric, incremental estimate of confidence thresholds, local learning.
result Proposed methods outperform non-online counterparts in various scenarios.
Random forest can be adapted for open-set recognition with improved performance.
problem Handling unknown classes in real-world classification tasks.
method Incorporating distance metric learning and distance-based open-set recognition into random forest.
result The proposed method outperforms state-of-the-art open-set recognition methods.
A two-step approach uses knowledge graphs for open-world image recognition.
problem Predicting unknown class labels in real-world visual recognition.
method Learn a knowledge-graph representation and an image representation, then predict relationship triples.
result Predictions for unknown class labels are semantically meaningful.
OpenHAIV integrates OOD detection and incremental learning for open-world models.
problem Challenges in open-world recognition, especially in model knowledge updates and OOD detection.
method Unified pipeline combining OOD detection, new class discovery, and incremental fine-tuning.
result Models can autonomously acquire and update knowledge in open-world environments.
Neural approach enhances AI trustworthiness, generalization, and robustness.
problem Challenges in explaining, generalizing, and adapting AI models to uncertain environments.
method Customized trustworthy networks, flexible learning regularizers, open-world recognition losses.
result Significant performance improvements across various open-world multimedia recognition scenarios.
This work bridges continual learning, active learning, and open set recognition in deep neural networks.
problem Protecting previously acquired representations from catastrophic forgetting in deep neural networks.
method Surveying the literature and proposing a consolidated view to integrate open set recognition and active learning principles.
result Joint improvement in alleviating catastrophic forgetting, querying data, selecting task orders, and robust open world application.
Framework for continual object recognition in egocentric settings.
problem Recognizing objects in a cold-start, open-world setting with limited supervision.
method Memory-based incremental framework using time and space persistence, similarity, and active learning.
result Feasibility of open-world, generic object recognition with complete user supervision.
This paper reviews information theory in open-world machine learning.
problem Lack of a unified theoretical foundation for open-world machine learning.
method Synthesis of information theoretic approaches.
result Established a pathway toward provable and trustworthy open world intelligence.
This article reviews zero-shot recognition techniques for unseen categories.
problem Scaling recognition to many classes with few training samples.
method Comprehensive review of existing zero-shot recognition techniques.
result Highlighting limitations and future directions in zero-shot recognition.
CGDL improves open set recognition by learning conditional Gaussian distributions.
problem Handling unknown samples in real-world recognition tasks.
method Conditional Gaussian Distribution Learning (CGDL) with probabilistic ladder architecture.
result CGDL significantly outperforms baseline methods on standard image datasets.
Proposes a new framework for open set recognition using conditional probabilistic generative models.
problem Unknown samples can mislead traditional deep neural networks during testing.
method Conditional Probabilistic Generative Models (CPGM) that combine generative models with discriminative information.
result Significantly outperforms baselines on multiple benchmark datasets.
Study benchmarks machine learning for removing EEG artifacts.
problem Removing artifacts from EEGs to improve clinical interpretation.
method Applied various machine learning algorithms to a large artifact recognition dataset.
result Established a benchmark for future research on artifact removal.
OSSVM extends SVM for open-set recognition, ensuring proper unknown class rejection.
problem Dealing with unknown classes in real-world recognition problems.
method Introducing OSSVM, balancing empirical and unknown risks.
result Ensures bounded region for known classes, finite risk of unknown.
OBSER framework infers sub-environments from objects, outperforming scene-based methods.
problem Zero-shot recognition of environments from object distributions.
method Bayesian framework using metric and self-supervised learning models to estimate object distributions in latent space.
result OBSER framework reliably performs inference in open-world and photorealistic environments, outperforming scene-based methods.
End-to-end framework learns new classes dynamically.
problem Challenges in recognizing unseen classes in real-world settings.
method Dynamic cascade of classifiers that incrementally learn features.
result Outperforms existing methods on real-world datasets.
Paper presents a neural network for open set recognition.
problem Open set recognition in security and other domains.
method Neural network representation for same class closeness and different class separation.
result Statistically significant improvement on three datasets.
New analysis shows uncertainty-based methods alone aren't enough for open set recognition.
problem Overcoming the challenge of recognizing out-of-distribution data.
method Comparing predictive uncertainty with extreme value theory and generative models.
result Generative model-based open set recognition outperforms other methods.
End-to-end open-set recognition using intra-class splitting.
problem Open-set recognition with limited known samples.
method Intra-class data splitting to model unknown classes.
result Outperformed baselines and improved state-of-the-art methods.
Solves recognition problem of frontal singularities.
problem Recognition of frontal singularities.
method Specified geometric frontal singularities, provided explicit normal forms, combined results from K. Saji and applied to tangent surfaces of null curves.
result Classification of singularities in tangent surfaces of null curves.
This study applies variational inference to improve music emotion recognition.
problem Improving understanding and recognition of music emotions.
method Employed variational inference and Bayesian statistics techniques.
result Developed a flexible multivariate model for emotion recognition.
A new method embeds visual features into semantic space for open-set recognition.
problem Learning unseen classes in open-set recognition.
method Vocabulary-informed Extreme Value Learning (ViEVL) combining EVL and ViL.
result ViEVL embeds visual features into semantic space probabilistically, solving open-set recognition.
Paper proposes a new decision strategy for open set recognition.
problem Existing OSR methods are limited in recognizing unknown classes and setting decision thresholds.
method Introduces a collective decision-based OSR framework (CD-OSR) using Hierarchical Dirichlet process (HDP).
result CD-OSR can simultaneously implement open set recognition and new class discovery.
This paper proposes a unified framework for recognizing seen and unseen classes using visual and semantic prototypes.
problem Class overfitting and misclassification of unseen classes in zero-shot learning.
method Decomposes G-ZSL into OSR and ZSL, introduces semantic side-information for OSR, and uses a VSG-CNN framework.
result Improves recognition performance and cognitive ability for unknown classes.
Survey on deep learning for malware classification, including unknown threats.
problem Classifying and recognizing unknown malware variants.
method Review of deep learning techniques and OSR solutions.
result Deep learning can effectively classify known malware and recognize unknown threats.
Unified approach to continual learning using generative replay and open set recognition.
problem Catastrophic interference and recognition of out-of-distribution data in deep neural networks.
method Probabilistic approach based on variational inference in a deep autoencoder model, using generative replay and open set recognition.
result The approach significantly alleviates catastrophic interference and distinguishes out-of-distribution data.
Deep learning applied to biological data mining.
problem Mining complex biological data from diverse sources.
method Artificial neural networks, deep learning architectures.
result Deep learning techniques improve pattern recognition in biological data.
Statistical method recognizes driving styles using vehicle speed and throttle opening.
problem Recognizing driving styles for improved vehicle performance and safety.
method Bayesian probability and kernel density estimation to describe driving styles uncertainty.
result Classifies driving styles into seven levels based on vehicle speed and throttle opening.
OpenViewer tackles multi-view learning challenges with interpretability and generalization.
problem Lack of interpretability and insufficient generalization in multi-view learning models.
method OpenViewer introduces a Pseudo-Unknown Sample Generation Mechanism, Expression-Enhanced Deep Unfolding Network, and Perception-Augmented Open-Set Training Regime.
result OpenViewer effectively addresses openness challenges and enhances recognition performance for both known and unknown samples.
Unified framework improves object recognition from limited labeled data.
problem Challenges in object categorization, especially from limited labeled data.
method Semi-supervised vocabulary-informed learning with maximum margin framework.
result Improvements in supervised, zero-shot, and open set recognition.
CAT is a new ASR toolkit using CRF and CTC for state-of-the-art speech recognition.
problem Improving automatic speech recognition systems.
method CRF-based discriminative training with CTC-inspired state topology.
result CAT achieves state-of-the-art results with fewer parameters and is competitive with hybrid models.
Challenge aims to recognize music genres from audio.
problem Recognizing music genres from audio recordings.
method Open data challenge with submissions evaluated.
result Results presented from the challenge.
FSD50K provides an open dataset of over 51k audio clips for sound event recognition.
problem Small and domain-specific sound event recognition datasets.
method Creation of an open dataset with over 51k audio clips manually labeled using 200 classes.
result FSD50K is a new open benchmark for sound event recognition research.
Improved deep learning for one-shot and open-set classification using alignment-based matching.
problem Limited data for one-shot classification and open-set recognition.
method Aligns images to reference images for classification, learns alignment mechanism.
result Significantly improved classification accuracy (e.g., 1.4% error rate in Omniglot, 46.5% in MiniImageNet).
Improves few-shot learning for real-world recognition with novel methods.
problem Challenges in real-world recognition with heavy-tailed class distributions and cluttered scenes.
method Parameter-free improvements including better training procedures, object localization, and feature space expansion.
result Doubles accuracy of state-of-the-art models on meta-iNat while generalizing to diverse settings.
GraphShield uses dynamic graph learning to detect and visualize financial risks.
problem Detecting and mitigating risks in financial networks.
method Enhanced Cross-Domain Information Learning, Advanced Risk Recognition, Risk Propagation Visualization.
result GraphShield effectively identifies and visualizes hidden financial risks.
New CNNs learn features from unlabeled data for better activity recognition.
problem Limited labeled data for activity recognition leads to poor generalization.
method Semi-supervised CNNs that learn features from raw sensor data.
result Semi-supervised CNNs outperform supervised and traditional methods by up to 18%.
The Familiarity Hypothesis explains deep open set methods' success in detecting novel objects.
problem Detecting novel objects in open set recognition problems.
method Logits-based detection of absence of familiar features.
result Familiarity-based detection fails in scenarios with both novel and familiar objects.
System supports 102 languages with deep neural networks, reducing error rates and improving recognition speed.
problem Online handwriting recognition for multiple languages with high accuracy and speed.
method Deep neural network architecture, Bézier curves for input encoding, sequence recognition methods.
result Reduced error rates by 20%-40% for most languages, up to 10x faster recognition times.
A new method trains deep neural networks for open set domain adaptation without negative open set difference.
problem Training deep neural networks for open set domain adaptation without negative open set difference.
method Proposes a new upper bound of target-domain risk, including source-domain risk, ε-open set difference (Δε), distributional discrepancy, and constant. Uses gradient descent for source-domain risk and Δε, and adversarial training for distributional discrepancy. Trains DNNs via minimizing the new upper bound. result Shows state-of-the-art performance on benchmark datasets.
Deep learning improves speaker recognition verification and identification.
problem Limited progress in speaker recognition for 5-6 years.
method Applied deep learning techniques in speaker verification and identification.
result Deep learning becomes the state-of-the-art solution for speaker recognition.
End-to-end speech recognition system trained on GPUs and CPUs.
problem Building state-of-the-art speech recognition systems.
method Utilizes CPUs and GPUs for training, data augmentation, and neural network updates. Uses vocal tract length perturbation and acoustic simulator for data augmentation. Employed Horovod allreduce for training.
result Achieved 7.92% WER on proprietary English Bixby open domain test set using a Bidirectional Full Attention (BFA) model.
ViewFool identifies adversarial viewpoints to test image recognition robustness.
problem Lack of robustness to viewpoint changes in visual recognition models.
method Neural Radiance Fields (NeRF) and entropic regularizer to find adversarial viewpoints.
result Common image classifiers are highly vulnerable to generated adversarial viewpoints.
Paper proposes a loss extension for neural networks to improve OSR performance.
problem Open set recognition problem, distinguishing known and unknown classes.
method Introduces a loss function extension to find more discriminative polar representations.
result Significantly improves performance on datasets from different domains.
A new model disentangles object recognition and dynamics from video data.
problem Temporal reasoning in dynamically changing video data.
method Kalman variational auto-encoder framework for unsupervised learning.
result Model disentangles object representation and dynamics, outperforming other methods.
This research generates synthetic data streams for handling concept drifts and novel classes.
problem Handling concept drifts and novel classes in dynamic data streams.
method Synthetic data stream generation for both concept drifts and novel classes.
result Demonstrates the effectiveness of unsupervised drift detectors in open set recognition.
Gesture recognition system for automated bartending.
problem Improving efficiency and accuracy in bar ordering.
method Machine Learning for gesture classification using collected data.
result Average accuracy of 95% in gesture recognition.
HuSpaCy offers an industrial-grade Hungarian NLP toolkit.
problem Lack of suitable open-source Hungarian NLP pipelines.
method Built on spaCy, HuSpaCy includes lemmatization, morphosyntactic analysis, entity recognition, and word embeddings.
result HuSpaCy achieves high accuracy with resource-efficient prediction.