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07/20/2023
Representation Learning in Anomaly Detection: Successes, Limits and a Grand Challenge
In this perspective paper, we argue that the dominant paradigm in anomal...
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06/12/2023
No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection
Anomaly detection methods, powered by deep learning, have recently been ...
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03/30/2023
Unsupervised Word Segmentation Using Temporal Gradient Pseudo-Labels
Unsupervised word segmentation in audio utterances is challenging as, in...
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02/23/2023
Set Features for Fine-grained Anomaly Detection
Fine-grained anomaly detection has recently been dominated by segmentati...
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02/02/2023
Dreamix: Video Diffusion Models are General Video Editors
Text-driven image and video diffusion models have recently achieved unpr...
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12/01/2022
Attribute-based Representations for Accurate and Interpretable Video Anomaly Detection
Video anomaly detection (VAD) is a challenging computer vision task with...
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12/01/2022
Improving Zero-Shot Models with Label Distribution Priors
Labeling large image datasets with attributes such as facial age or obje...
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11/17/2022
Conffusion: Confidence Intervals for Diffusion Models
Diffusion models have become the go-to method for many generative tasks,...
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10/19/2022
Anomaly Detection Requires Better Representations
Anomaly detection seeks to identify unusual phenomena, a central task in...
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07/07/2022
Red PANDA: Disambiguating Anomaly Detection by Removing Nuisance Factors
Anomaly detection methods strive to discover patterns that differ from t...
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04/27/2022
Unsupervised Word Segmentation using K Nearest Neighbors
In this paper, we propose an unsupervised kNN-based approach for word se...
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03/21/2022
A Contrastive Objective for Learning Disentangled Representations
Learning representations of images that are invariant to sensitive or un...
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03/10/2022
An Empirical Investigation of 3D Anomaly Detection and Segmentation
Anomaly detection and segmentation in images has made tremendous progres...
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02/08/2022
Time Series Anomaly Detection by Cumulative Radon Features
Detecting anomalous time series is key for scientific, medical and indus...
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12/14/2021
Out-of-Distribution Detection without Class Labels
Anomaly detection methods identify samples that deviate from the normal ...
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12/06/2021
Semantic Segmentation In-the-Wild Without Seeing Any Segmentation Examples
Semantic segmentation is a key computer vision task that has been active...
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10/09/2021
The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design
Pretraining Neural Language Models (NLMs) over a large corpus involves c...
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09/13/2021
Image Shape Manipulation from a Single Augmented Training Sample
In this paper, we present DeepSIM, a generative model for conditional im...
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06/29/2021
An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wild
Unsupervised disentanglement has been shown to be theoretically impossib...
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06/07/2021
Mean-Shifted Contrastive Loss for Anomaly Detection
Deep anomaly detection methods learn representations that separate betwe...
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04/08/2021
The Single-Noun Prior for Image Clustering
Self-supervised clustering methods have achieved increasing accuracy in ...
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03/25/2021
Scaling-up Disentanglement for Image Translation
Image translation methods typically aim to manipulate a set of labeled a...
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02/15/2021
Reconstruction-Based Membership Inference Attacks are Easier on Difficult Problems
Membership inference attacks (MIA) try to detect if data samples were us...
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10/12/2020
PANDA – Adapting Pretrained Features for Anomaly Detection
Anomaly detection methods require high-quality features. One way of obta...
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07/09/2020
Improving Style-Content Disentanglement in Image-to-Image Translation
Unsupervised image-to-image translation methods have achieved tremendous...
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07/02/2020
Deep Single Image Manipulation
Image manipulation has attracted much research over the years due to the...
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05/05/2020
Classification-Based Anomaly Detection for General Data
Anomaly detection, finding patterns that substantially deviate from thos...
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05/05/2020
Sub-Image Anomaly Detection with Deep Pyramid Correspondences
Nearest neighbor (kNN) methods utilizing deep pre-trained features exhib...
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04/07/2020
Training End-to-end Single Image Generators without GANs
We present AugurOne, a novel approach for training single image generati...
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02/24/2020
Deep Nearest Neighbor Anomaly Detection
Nearest neighbors is a successful and long-standing technique for anomal...
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11/27/2019
Crypto-Oriented Neural Architecture Design
As neural networks revolutionize many applications, significant privacy ...
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06/27/2019
Latent Optimization for Non-adversarial Representation Disentanglement
Disentanglement between pose and content is a key task for artificial in...
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12/21/2018
Non-Adversarial Image Synthesis with Generative Latent Nearest Neighbors
Unconditional image generation has recently been dominated by generative...
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12/14/2018
Towards Unsupervised Single-Channel Blind Source Separation using Adversarial Pair Unmix-and-Remix
Unsupervised single-channel blind source separation is a long standing s...
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11/30/2018
Neural separation of observed and unobserved distributions
Separating mixed distributions is a long standing challenge for machine ...
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06/03/2018
NAM: Non-Adversarial Unsupervised Domain Mapping
Several methods were recently proposed for the task of translating image...
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04/01/2018
Unsupervised Correlation Analysis
Linking between two data sources is a basic building block in numerous c...
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01/18/2018
An Iterative Closest Point Method for Unsupervised Word Translation
Unsupervised word translation from non-parallel inter-lingual corpora ha...
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06/19/2017
VAIN: Attentional Multi-agent Predictive Modeling
Multi-agent predictive modeling is an essential step for understanding p...
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06/07/2015
Visual Learning of Arithmetic Operations
A simple Neural Network model is presented for end-to-end visual learnin...
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11/27/2014