Open Set Learning
89 papers with code • 0 benchmarks • 6 datasets
Traditional supervised learning aims to train a classifier in the closed-set world, where training and test samples share the same label space. Open set learning (OSL) is a more challenging and realistic setting, where there exist test samples from the classes that are unseen during training. Open set recognition (OSR) is the sub-task of detecting test samples which do not come from the training.
Benchmarks
These leaderboards are used to track progress in Open Set Learning
Libraries
Use these libraries to find Open Set Learning models and implementations
MIMII
NINCO
ImageNet-1k vs NINCO
UCCS
Latest papers
Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation
rafaelvareto/maximal-entropy-loss
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Open-set face recognition refers to a scenario in which biometric systems have incomplete knowledge of all existing subjects.
OpenGCD: Assisting Open World Recognition with Generalized Category Discovery
To bridge this gap, we propose OpenGCD that combines three key ideas to solve the above problems sequentially: (a) We score the origin of instances (unknown or specifically known) based on the uncertainty of the classifier's prediction; (b) For the first time, we introduce generalized category discovery (GCD) techniques in OWR to assist humans in grouping unlabeled data; (c) For the smooth execution of IL and GCD, we retain an equal number of informative exemplars for each class with diversity as the goal.
HomOpt: A Homotopy-Based Hyperparameter Optimization Method
Traditional methods, like grid search and Bayesian optimization, often struggle to quickly adapt and efficiently search the loss landscape.
Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology
Developing computational pathology models is essential for reducing manual tissue typing from whole slide images, transferring knowledge from the source domain to an unlabeled, shifted target domain, and identifying unseen categories.
Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds
lhrst/ase
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Open-Set Recognition (OSR) is dedicated to addressing the unknown class issue, but existing OSR methods are not designed to model the semantic information of the unseen classes.
Towards Open Vocabulary Learning: A Survey
jianzongwu/awesome-open-vocabulary
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To our knowledge, this is the first comprehensive literature review of open vocabulary learning.
Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting Systems
mrusci/ondevice-fewshot-kws
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A personalized KeyWord Spotting (KWS) pipeline typically requires the training of a Deep Learning model on a large set of user-defined speech utterances, preventing fast customization directly applied on-device.
Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution Shifts
pritamqu/OOD-VSSL
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NeurIPS 2023
Video self-supervised learning (VSSL) has made significant progress in recent years.
In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation
j-cb/ninco
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The OOD detection performance when the in-distribution (ID) is ImageNet-1K is commonly being tested on a small range of test OOD datasets.
torchosr -- a PyTorch extension package for Open Set Recognition models evaluation in Python
w4k2/torchosr
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The package offers two state-of-the-art methods in the field, a set of functions for handling base sets and generation of derived sets for the Open Set Recognition task (where some classes are considered unknown and used only in the testing process) and additional tools to handle datasets and methods.
