ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1909.11480
  4. Cited By
Input complexity and out-of-distribution detection with likelihood-based
  generative models

Input complexity and out-of-distribution detection with likelihood-based generative models

25 September 2019
Joan Serrà
David Álvarez
Vicencc Gómez
Olga Slizovskaia
José F. Núñez
Jordi Luque
    OODD
ArXivPDFHTML

Papers citing "Input complexity and out-of-distribution detection with likelihood-based generative models"

18 / 68 papers shown
Title
Taxonomy of Machine Learning Safety: A Survey and Primer
Taxonomy of Machine Learning Safety: A Survey and Primer
Sina Mohseni
Haotao Wang
Zhiding Yu
Chaowei Xiao
Zhangyang Wang
J. Yadawa
21
31
0
09 Jun 2021
Do We Really Need to Learn Representations from In-domain Data for
  Outlier Detection?
Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?
Zhisheng Xiao
Qing Yan
Y. Amit
OOD
UQCV
20
18
0
19 May 2021
Autoencoding Under Normalization Constraints
Autoencoding Under Normalization Constraints
Sangwoong Yoon
Yung-Kyun Noh
Frank C. Park
OODD
UQCV
27
38
0
12 May 2021
Distribution Awareness for AI System Testing
Distribution Awareness for AI System Testing
David Berend
24
8
0
06 May 2021
MOS: Towards Scaling Out-of-distribution Detection for Large Semantic
  Space
MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space
Rui Huang
Yixuan Li
OODD
39
236
0
05 May 2021
MOOD: Multi-level Out-of-distribution Detection
MOOD: Multi-level Out-of-distribution Detection
Ziqian Lin
Sreya . Dutta Roy
Yixuan Li
OODD
34
114
0
30 Apr 2021
Autoencoders for unsupervised anomaly detection in high energy physics
Autoencoders for unsupervised anomaly detection in high energy physics
Thorben Finke
Michael Krämer
A. Morandini
A. Mück
I. Oleksiyuk
21
83
0
19 Apr 2021
Unsupervised Class-Incremental Learning Through Confusion
Unsupervised Class-Incremental Learning Through Confusion
Shivam Khare
Kun Cao
James M. Rehg
SSL
CLL
24
6
0
09 Apr 2021
Flow-based Self-supervised Density Estimation for Anomalous Sound
  Detection
Flow-based Self-supervised Density Estimation for Anomalous Sound Detection
Kota Dohi
Takashi Endo
Harsh Purohit
Ryo Tanabe
Y. Kawaguchi
22
58
0
16 Mar 2021
Feature Space Singularity for Out-of-Distribution Detection
Feature Space Singularity for Out-of-Distribution Detection
Haiwen Huang
Zhihan Li
Lulu Wang
Sishuo Chen
Bin Dong
Xinyu Zhou
OODD
22
65
0
30 Nov 2020
Further Analysis of Outlier Detection with Deep Generative Models
Further Analysis of Outlier Detection with Deep Generative Models
Ziyu Wang
Bin Dai
David Wipf
Jun Zhu
17
39
0
25 Oct 2020
Energy-based Out-of-distribution Detection
Energy-based Out-of-distribution Detection
Weitang Liu
Xiaoyun Wang
John Douglas Owens
Yixuan Li
OODD
107
1,298
0
08 Oct 2020
CSI: Novelty Detection via Contrastive Learning on Distributionally
  Shifted Instances
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
Jihoon Tack
Sangwoo Mo
Jongheon Jeong
Jinwoo Shin
OODD
11
588
0
16 Jul 2020
Detecting Out-of-distribution Samples via Variational Auto-encoder with
  Reliable Uncertainty Estimation
Detecting Out-of-distribution Samples via Variational Auto-encoder with Reliable Uncertainty Estimation
Xuming Ran
Mingkun Xu
Lingrui Mei
Qi Xu
Quanying Liu
OODD
UQCV
42
50
0
16 Jul 2020
Density of States Estimation for Out-of-Distribution Detection
Density of States Estimation for Out-of-Distribution Detection
Warren Morningstar
Cusuh Ham
Andrew Gallagher
Balaji Lakshminarayanan
Alexander A. Alemi
Joshua V. Dillon
OODD
22
83
0
16 Jun 2020
Hybrid Models for Open Set Recognition
Hybrid Models for Open Set Recognition
Hongjie Zhang
Ang Li
Jie Guo
Yanwen Guo
BDL
28
184
0
27 Mar 2020
History-based Anomaly Detector: an Adversarial Approach to Anomaly
  Detection
History-based Anomaly Detector: an Adversarial Approach to Anomaly Detection
Pierrick Chatillon
C. Ballester
AAML
21
6
0
26 Dec 2019
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,675
0
05 Dec 2016
Previous
12