- Contrastive Training for Improved Out-of-Distribution Detection
- Breaking the Closed World Assumption in Text Classification
- Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data
- BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty
- P-ODN: Prototype based Open Deep Network for Open Set Recognition
- Learning a Neural-network-based Representation for Open Set Recognition
- Adversarial Network with Multiple Classifiers for Open Set Domain Adaptation
- From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer
- A Benchmark for Anomaly Segmentation
- Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
- Towards Open Set Deep Networks
- Density of States Estimation for Out-of-Distribution Detection
- Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation
- Specialized Support Vector Machines for Open-set Recognition
- Open Set Domain Adaptation: Theoretical Bound and Algorithm
- Learning Open Set Network with Discriminative Reciprocal Points
- Learning to Separate Domains in Generalized Zero-Shot and Open Set Learning: a probabilistic perspective
- Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks
- Large-Scale Long-Tailed Recognition in an OpenWorld
- On the EFFectiveness of Image Rotation for Open Set Domain Adaptation
- The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
- Open Set Domain Adaptation by Backpropagation
- Likelihood Ratios for Out-of-Distribution Detection
- Open Set Recognition with Conditional Probabilistic Generative Models
- MMF: A loss extension for feature learning in open set recognition
- Adversarial Robustness: Softmax versus Openmax
- Separate to Adapt: Open Set Domain Adaptation via Progressive Separation
- Conditional Gaussian Distribution Learning for Open Set Recognition
- A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
- S2OSC: A Holistic Semi-Supervised Approach for Open Set Classification
- Input complexity and out-of-distribution detection with likelihood-based generative models
- Fully Convolutional Open Set Segmentation
- Towards OpenWorld Recognition
- ProvableWorst Case Guarantees for the Detection of Out-of-Distribution Data
- The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning
- Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation
- Open Set Domain Adaptation
- Known-class Aware Self-ensemble for Open Set Domain Adaptation
- Label Efficient Learning of Transferable Representations across Domains and Tasks
- Deep Transfer Learning for Multiple Class Novelty Detection
- C2AE: Class Conditioned Auto-Encoder for Open-set Recognition
- One-vs-Rest Network-based Deep Probability Model for Open Set Recognition
- Hybrid Models for Open Set Recognition
- Classification-Reconstruction Learning for Open-Set Recognition
- Attract or Distract: Exploit the Margin of Open Set
- Deep CNN-based Multi-task Learning for Open-Set Recognition
- Progressive Graph Learning for Open-Set Domain Adaptation
- Do Deep Generative Models Know What They Don't Know?
- OpenGAN: Open Set Generative Adversarial Networks
- Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem
- Open Set Domain Adaptation with Multi-Classifier Adversarial Network
- Fast and Accurate Face Recognition with Image Sets
- Online Open World Recognition
- Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization
- Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
- Learning Cumulatively to Become More Knowledgeable
- Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
- WAIC, but Why? Generative Ensembles for Robust Anomaly Detection
- Open-World Visual Recognition Using Knowledge Graphs
- Collective Decision for Open Set Recognition
- Open-world Learning and Application to Product Classification
- Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?
- Open Set Text Classification using Convolutional Neural Networks
- Deep Anomaly Detection Using Geometric Transformations
- Polyhedral Conic Classifiers for Visual Object Detection and Classification
- Collaborative Training of Balanced Random Forests for Open Set Domain Adaptation
- Generative OpenMax for Multi-Class Open Set Classification
- Open-Set Recognition Using Intra-Class Splitting
- Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration
- Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
- Open-Category Classification by Adversarial Sample Generation
- Query Attack via Opposite-Direction Feature: Towards Robust Image Retrieval
- Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks
- Alignment Based Matching Networks for One-Shot Classification and Open-Set Recognition
- Class Anchor Clustering: A Loss for Distance-based Open Set Recognition
- Classification-Based Anomaly Detection for General Data
- Open Set Domain Adaptation for Image and Action Recognition
- Open Category Detection with PAC Guarantees
- Deep Anomaly Detection with Outlier Exposure
- Unseen Class Discovery in Open-world Classification
- Outlier Exposure with Confidence Control for Out-of-Distribution Detection
- Extreme Value Theory for Open Set Classification - GPD and GEV Classifiers
- Learning and the Unknown: Surveying Steps Toward Open World Recognition
- The Effect of Optimization Methods on the Robustness of Out-of-Distribution Detection Approaches
- Learning Factorized Representations for Open-set Domain Adaptation
- DOC: Deep Open Classification of Text Documents
- Experiments on Open-Set Speaker Identification with Discriminatively Trained Neural Networks
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
- Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy