Unsupervised Error Detection through Clustering. Work performed at IISc Bangalore
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Updated
Jul 31, 2020 - Jupyter Notebook
Unsupervised Error Detection through Clustering. Work performed at IISc Bangalore
Attribute-Inference for out-of-set Detection
👾 Outlier Exposure with Generative Models
Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR2021).
[ECCV'22 Oral] Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes. Dealing with out-of-distribution detection or open-set recognition in semantic segmentation.
Epistemic uncertainty, sometimes referred to as model uncertainty, describes what the model does not know because training data was not appropriate. Modelling epistemic uncertainty is crucial to prevent ill advised discussion making due to over confident models.
Hugging Face Space for showcasing how out-of-distribution (OOD) detection works.
Implementation of "Multiple Hypothesis Testing for Anomaly Detection in Multi-type Event Sequences" (ICDM 2023)
Official PyTorch implementation of the ACM Multimedia 2024 paper “Improving Out-of-distribution Detection with Disentangled Foreground and Background Features”
Very simple version of the code used for the experiments in the paper Hidden Activations Are Not Enough: A General Approach to Neural Networks Predictions.
Official repository for the paper entitled "Feature-based Out-of-Distribution Detection for Medical Imaging Segmentation".
Out-of-Distribution Detection For Forgery Images Using Digital Watermarking
The accompanying code to the publication "Out-of-Distribution Detection using Outlier Detection Methods"
Implementation of ICCV'23 paper "Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation"
Python experiments for https://arxiv.org/abs/1904.12286.
Official PyTorch Implementation of Meta-Query-Net NeurIPS 2022.
Python library for analyzing data quality and its impact on model performance across classification and object-detection tasks.
Fooling Machine Learning Models: A Novel Out-of-Distribution Attack through Generative Adversarial Networks
Benchmarking Bayesian Deep Learning for Out-of-Distribution Detection
CSCI2470 Deep Learning Spring 2024: Enhancing Out-of-Distribution Object Detection with CLIP: A Vision-Language Approach
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