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.
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48 results for “open set setting”
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 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.
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.
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.
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.
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.
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.
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.
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 (), distributional discrepancy, and constant. Uses gradient descent for source-domain risk and , and adversarial training for distributional discrepancy. Trains DNNs via minimizing the new upper bound.
result Shows state-of-the-art performance on benchmark datasets.
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.
What can you draw?math.MG
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.
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 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.
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.
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.
Open problem: fixed-budget best arm identification complexity.
problem Understanding the complexity of identifying the best arm in a fixed budget setting.
method Analyzing existing results and conjectures in the fixed-confidence setting.
result Open questions remain about the fixed-budget setting.
Study shows neural networks outperform traditional methods in speaker identification.
problem Open-set speaker identification with large populations.
method Discriminative neural networks compared to Gaussian mixture models.
result Multi-class neural networks outperform traditional methods for large speaker populations.
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.
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 …
Often, when dealing with real-world recognition problems, we do not need, and often cannot have, knowledge of the entire set of possible classes that might appear during operational testing. In such cases, we need to think of robust classification methods able to deal with the "unknown" and properly reject samples belo…
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.
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.
The compact-open topology on the homeomorphism group of a surface without boundary is minimalmath.GT
Minimal topology on surface homeomorphisms proven.
problem Proving the compact-open topology is minimal for surface homeomorphisms.
method Combining Hausdorff group topology properties and automatic continuity results.
result Compact-open topology is unique Hausdorff separable group topology on surface homeomorphisms.
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.
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.
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.
Paper develops a new method for open-set and imbalanced classification with valid prediction sets.
problem Tackles open-set and imbalanced classification with new prediction methods.
method Develops a new family of conformal p-values and a selective sample splitting algorithm.
result Valid prediction sets with valid coverage in open-set scenarios and informative predictions under extreme class imbalance.
For each Cantor set C in , all points of which have bounded local genus, we show that there are infinitely many inequivalent Cantor sets in with complement having the same fundamental group as the complement of C. This answers a question from Open Problems in Topology and has as an application a simple c…
Free groups can be end homogeneity groups of 3-manifolds.
problem Tackling the possibility of free groups as end homogeneity groups of 3-manifolds.
method Constructing specific 3-manifolds with end homogeneity groups isomorphic to free groups.
result For every finitely generated free group, there exists an irreducible open 3-manifold with that group as its end homogeneity group.
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…
In the paper arXiv:1411.4887 [math.AP] it is shown that the set of Riemannian metrics which do not admit global limiting Carleman weights is open and dense, by studying the conformally invariant Weyl and Cotton tensors. In the paper arXiv:1011.2507 [math.DG] it is shown that the set of Riemannian metrics which do not a…
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).
Random 3-manifolds have no totally geodesic submanifolds.
problem Existence of totally geodesic submanifolds in random 3-manifolds.
method Analysis of metrics on compact 3-manifolds in -topology.
result The set of such metrics contains an open and dense set in the -topology for any .
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.
New bounds on inscribed triangles in arbitrary planar domains.
problem Finding inscribed triangles in arbitrary planar domains with specific angle constraints.
method Proving the existence of uniformly fat triangles and not-too-fat triangles in bounded open sets.
result Existence of a maximal number Θ (between 0 and 60) for inscribed triangles with angles ≥ Θ degrees.
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.
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.
The paper proves removable singularity for nonlocal minimal graphs.
problem Proving removable singularities for nonlocal minimal graphs.
method Analyzing -capacity zero compact sets to ensure graphs are minimal in the entire domain.
result Nonlocal minimal graphs are removable in the entire domain if they are minimal in a set of -capacity zero.
Semi-monotone setsmath.LO
A coordinate cone in R^n is an intersection of some coordinate hyperplanes and open coordinate half-spaces. A semi-monotone set is a defnable in an o-minimal structure over the reals, open bounded subset of R^n such that its intersection with any translation of any coordinate cone is connected. This can be viewed as a …
In a model independent discrete time financial market, we discuss the richness of the family of martingale measures in relation to different notions of Arbitrage, generated by a class of significant sets, which we call Arbitrage de la classe . The choice of reflects into the int…
We construct uncountably many simply connected open 3-manifolds with genus one ends homeomorphic to the Cantor set. Each constructed manifold has the property that any self homeomorphism of the manifold (which necessarily extends to a homeomorphism of the ends) fixes the ends pointwise. These manifolds are complements …
Study shows range of simplicial volumes for open manifolds.
problem Understanding simplicial volumes of open manifolds.
method Analyzes locally finite simplicial volumes in dimensions at least 4.
result Set of simplicial volumes is [0, ∞] for open manifolds.
The paper proves smoothness of stationary varifolds.
problem Understanding the smoothness of stationary varifolds.
method Analyzing -dimensional integer rectifiable varifolds in open sets.
result The support of stationary varifolds is rectifiable.
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.
Novikov's problem of semiclassical orbits of quasi-electrons in a normal metal leads to a correspondance between 3-ply periodic functions in R and fractals in R P^2. These fractals are the complement of infinitely many open sets labeled by integer 2-cycles of T^3. Here we present a characterization of the fractal point…
Open category detection is the problem of detecting "alien" test instances that belong to categories or classes that were not present in the training data. In many applications, reliably detecting such aliens is central to ensuring the safety and accuracy of test set predictions. Unfortunately, there are no algorithms …