Research improves open-set learning by leveraging unlabelled data.
problem Learning between observed and unobserved novel categories.
method Unified policy of positive and unlabelled learning, semi-supervised learning, and open-set recognition.
result Achieves state-of-the-art results in open-set learning.
End-to-end PGL framework tackles open-set domain shift.
problem Real-world domain shift with unknown additional classes.
method Episodic training in graph neural network with adversarial learning.
result Guarantees tighter upper bound of target error.
Study on diagnosing unseen medical conditions using open-set learning.
problem Training models for unseen medical conditions is impractical.
method Frame diagnosis as an open-set learning problem, compare state-of-the-art approaches, and experiment with distributed training data.
result Explicitly modeling unseen conditions leads to consistent gains, but optimal training strategy varies.
Paper tackles open set domain adaptation with theoretical bounds and algorithms.
problem Improving model performance on target domain with unknown classes.
method Theoretical investigation and regularization of open set difference, leading to DAOD algorithm.
result Proposed learning bound and algorithm show superior performance compared to existing methods.
Distribution networks model novel classes in open set learning.
problem Modeling novel classes in open set learning.
method Distribution networks map samples to a latent space where known and novel classes' distributions are jointly learned.
result Distribution networks accurately detect and model novel classes for subsequent classification.
Integrates multiple datasets to solve open set crowdsourcing problems.
problem Crowdsourcing with unknown label space and unfamiliar tasks.
method Integrates multiple crowdsourced datasets, weights them based on category correlation, and uses open set transfer learning.
result Proves OSCrowd solves open set crowdsourcing problems and outperforms related solutions.
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.
GMVAE improves open-set classification by clustering latent representations.
problem Improving open-set classification accuracy and robustness.
method Cooperative learning of reconstruction and clustering in the latent space of a GMVAE.
result Achieved an average F1 improvement of 29.5% in open-set classification.
Paper develops a deep neural network for open set incremental learning of new authors.
problem Classifying unseen examples from previously unseen classes.
method Deep neural network clustering and retraining for new classes.
result Incremental learning model that continuously learns new classes.
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.
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.
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.
New method improves OSSL by learning from all unlabeled data.
problem Handling open-set semi-supervised learning with unknown classes.
method Self-supervision and energy-based score for all unlabeled data.
result State-of-the-art results on benchmark problems.
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.
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.
FedOS tackles challenges in federated learning by using open-set learning.
problem Challenges in federated learning due to data locality and privacy constraints.
method Introduces open-set learning to stabilize training in federated learning.
result Demonstrates improved model performance through open-set learning.
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).
SMART simplifies data labeling for machine learning.
problem Creating labeled training data for machine learning.
method Intuitive web interface, active learning, inter-rater reliability.
result Reduces the need for labeled data and improves label quality.
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.
Open set recognition problems exist in many domains. For example in security, new malware classes emerge regularly; therefore malware classification systems need to identify instances from unknown classes in addition to discriminating between known classes. In this paper we present a neural network based representation…
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.
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.
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.
This paper reviews FSL for open-world learning, addressing uncertainties and dynamic conditions.
problem Adapting FSL for open-world settings with varying instances, classes, and distributions.
method Categorizes existing open-world FSL methods into three types and discusses their challenges and strengths.
result Standardized experimental settings and benchmarks for comparative analysis of methods.
The VC-dimension of a set system is a way to capture its complexity and has been a key parameter studied extensively in machine learning and geometry communities. In this paper, we resolve two longstanding open problems on bounding the VC-dimension of two fundamental set systems: k-fold unions/intersections of half-s…
Method transfers knowledge between partially labeled domains to classify all samples.
problem Weakly supervised open-set domain adaptation between partially labeled domains.
method Collaborative Distribution Alignment (CDA) method for bilaterally knowledge transfer and outlier identification.
result Achieves state-of-the-art performance on Office benchmark and person reidentification.
The novel unseen classes can be formulated as the extreme values of known classes. This inspired the recent works on open-set recognition \cite{Scheirer_2013_TPAMI,Scheirer_2014_TPAMIb,EVM}, which however can have no way of naming the novel unseen classes. To solve this problem, we propose the Extreme Value Learning (E…
Modern machine learning systems such as image classifiers rely heavily on large scale data sets for training. Such data sets are costly to create, thus in practice a small number of freely available, open source data sets are widely used. We suggest that examining the geo-diversity of open data sets is critical before …
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.
New method for estimating class proportions in open-set label shift data.
problem Estimating class proportions and distributions when test data includes novel classes.
method Semiparametric density ratio model framework with maximum empirical likelihood estimators and confidence intervals.
result Improved estimation accuracy and classification performance compared to existing methods.
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.
Despite significant progress in object categorization, in recent years, a number of important challenges remain, mainly, ability to learn from limited labeled data and ability to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it …
Paper explores methods to improve web search ranking.
problem Improving web search ranking accuracy with varied test data.
method Model Interpolation and Boosting Algorithm.
result Model Interpolation achieves best results on open test sets.
Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark's open-source distributed machine learning library. MLlib provides efficient functionality for a wide range of learning settings and includes sev…
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.
ProSub uses angles in feature space to classify data as in- or out-of-distribution.
problem Open-set semi-supervised learning with unknown classes.
method Probabilistic approach based on angles in feature space, estimating conditional distributions of scores.
result ProSub achieves state-of-the-art performance on benchmark problems.
Toolkit simplifies transfer learning for researchers.
problem Making transfer learning accessible to non-experts.
method Wraps 17 models, provides easy-to-use interfaces.
result Simplifies model selection for real-world applications.
Skew-Fit learns goal distributions for reinforcement learning, enabling state coverage.
problem Learning flexible skills without manual reward design.
method Formal exploration objective for maximizing state coverage, combined with entropy maximization.
result Skew-Fit converges to a uniform state distribution, enabling new skill learning.
Estimates open sets for fibrations, leading to volume vanishing results.
problem Estimating open sets for fibrations.
method Straightforward estimate for open sets with fundamental group constraints.
result Vanishing results for simplicial volume and minimal volume entropy for certain mapping tori.
The study connects periodic surface homeomorphisms to contact structures using rational open books.
problem Understanding the properties of contact structures associated with periodic surface homeomorphisms.
method Associate rational open books to marked data sets, study contact structures, and prove Stein fillability conditions.
result A class of data sets gives rise to Stein fillable contact structures under certain combinatorial conditions.
HRL improves open-domain dialog models by optimizing long-term conversational goals.
problem Challenges in open-domain dialog generation, including repetitive outputs, difficulty tracking conversational goals, and inappropriate text.
method Proposes VHRL, a hierarchical reinforcement learning approach using policy gradients to tune utterance-level embeddings of a variational sequence model.
result Significant improvements in human evaluation and automatic metrics over state-of-the-art dialog models.
OpenEDS dataset captures eye images for VR applications.
problem Lack of high-quality eye image datasets for VR applications.
method Captured eye images using VR head-mounted display with synchronized cameras.
result Baseline experiment achieved 98.3% mIoU for pupil, iris, sclera segmentation.
Study tackles open-set camera model identification, improving over state-of-the-art.
problem Identifying camera models from unknown ones in open-set scenarios.
method Feature extraction algorithms and classifiers for open-set recognition, evaluating different training protocols.
result A simple open-set training protocol yields the best results, improving over state-of-the-art solutions.
Open and discrete maps with specific branch set images are equivalent to PL branched covers.
problem Understanding the equivalence of open and discrete maps and PL branched covers.
method Demonstrated that an open and discrete map f:SnoSn with a specific branch set image is equivalent to a PL branched cover up to homeomorphism. result Open and discrete maps with a specific branch set image are equivalent to PL branched covers.
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.
This work tackles autonomous learning of interrelated tasks in robots.
problem Learning interrelated tasks in robots, especially in complex environments.
method Using a multi-task reinforcement learning approach within an MDP framework.
result Demonstrates how to autonomously learn interrelated tasks in robots.
Can certain shapes be drawn with a pencil and eraser?
problem Characterizing which planar sets can be drawn with a pencil and eraser.
method Analyzes the properties of sets drawable with a pencil and eraser, using open and closed unit disks.
result Drawability cannot be characterized by local obstructions.
Bayesian model adapts to changing classes in machine learning.
problem Non-stationary environments with incomplete class information.
method Doubly non-parametric Bayesian Gaussian mixture model.
result Model can grow to accommodate any number of classes.