Unsupervised Data Uncertainty Learning in Visual Retrieval Systems
We introduce an unsupervised formulation to estimate heteroscedastic uncertainty in retrieval systems. We propose an extension to triplet loss that models data uncertainty for each input. Besides improving performance, our formulation models local noise in the embedding space. It quantifies input uncertainty and thus enhances interpretability of the system. This helps identify noisy observations in query and search databases. Evaluation on both image and video retrieval applications highlight the utility of our approach. We highlight our efficiency in modeling local noise using two real-world datasets: Clothing1M and Honda Driving datasets. Qualitative results illustrate our ability in identifying confusing scenarios in various domains. Uncertainty learning also enables data cleaning by detecting noisy training labels.
Code (0)
등록된 구현이 없습니다.
Tasks
RetrievalTripletVideo RetrievalMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Unsupervised Adversarial Attacks on Deep Feature-based Retrieval with GAN
Studies show that Deep Neural Network (DNN)-based image classification models are vulnerable to maliciously constructed adversarial examples. However, little effort has been made to investigate how DNN-based image retrie…
image-classificationImage ClassificationImage RetrievalRetrievalUnsupervised Neural Quantization for Compressed-Domain Similarity Search
We tackle the problem of unsupervised visual descriptors compression, which is a key ingredient of large-scale image retrieval systems. While the deep learning machinery has benefited literally all computer vision pipeli…
Image RetrievalQuantizationRetrievalUncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation
We present UncertaintyRAG, a novel approach for long-context Retrieval-Augmented Generation (RAG) that utilizes Signal-to-Noise Ratio (SNR)-based span uncertainty to estimate similarity between text chunks. This span unc…
ChunkingLanguage ModelingLanguage ModellingLarge Language Model+3Towards Unsupervised Adversarial Document Detection in Retrieval Augmented Generation Systems
Retrieval augmented generation systems have become an integral part of everyday life. Whether in internet search engines, email systems, or service chatbots, these systems are based on context retrieval and answer genera…
Outlier DetectionAnswer GenerationU-CREAT: Unsupervised Case Retrieval using Events extrAcTion
The task of Prior Case Retrieval (PCR) in the legal domain is about automatically citing relevant (based on facts and precedence) prior legal cases in a given query case. To further promote research in PCR, in this paper…
Retrieval