Open Set Learning

47 papers with code • 0 benchmarks • 3 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 images which do not come from the training.

Most implemented papers

Towards Open Set Deep Networks

aadeshnpn/OSDN CVPR 2016

We present a methodology to adapt deep networks for open set recognition, by introducing a new model layer, OpenMax, which estimates the probability of an input being from an unknown class.

Learning a Neural-network-based Representation for Open Set Recognition

shrtCKT/opennet 12 Feb 2018

Open set recognition problems exist in many domains.

Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition

MrtnMndt/OCDVAE_ContinualLearning 28 May 2019

Modern deep neural networks are well known to be brittle in the face of unknown data instances and recognition of the latter remains a challenge.

Query Attack via Opposite-Direction Feature:Towards Robust Image Retrieval

layumi/A_reID 7 Sep 2018

Opposite-Direction Feature Attack (ODFA) effectively exploits feature-level adversarial gradients and takes advantage of feature distance in the representation space.

Adversarial Motorial Prototype Framework for Open Set Recognition

Xiaziheng89/Adversarial-Motorial-Prototype-Framework-for-Open-Set-Recognition 13 Jul 2021

Second, this paper proposes the adversarial motorial prototype framework (AMPF) based on the MPF.

Generalized Out-of-Distribution Detection: A Survey

jingkang50/oodsurvey 21 Oct 2021

In this survey, we first present a generic framework called generalized OOD detection, which encompasses the five aforementioned problems, i. e., AD, ND, OSR, OOD detection, and OD.

Sparse Representation-based Open Set Recognition

hezhangsprinter/SROSR 6 May 2017

We propose a generalized Sparse Representation- based Classification (SRC) algorithm for open set recognition where not all classes presented during testing are known during training.

AP18-OLR Challenge: Three Tasks and Their Baselines

Rithmax/Sub-band-Envelope-Features-Using-Frequency-Domain-Linear-Prediction 2 Jun 2018

The third oriental language recognition (OLR) challenge AP18-OLR is introduced in this paper, including the data profile, the tasks and the evaluation principles.

Classification-Reconstruction Learning for Open-Set Recognition

saketd403/CROSR CVPR 2019

Existing open-set classifiers rely on deep networks trained in a supervised manner on known classes in the training set; this causes specialization of learned representations to known classes and makes it hard to distinguish unknowns from knowns.

Large-Scale Long-Tailed Recognition in an Open World

zhmiao/OpenLongTailRecognition-OLTR CVPR 2019

We define Open Long-Tailed Recognition (OLTR) as learning from such naturally distributed data and optimizing the classification accuracy over a balanced test set which include head, tail, and open classes.