# ALTBI: Constructing Improved Outlier Detection Models via Optimization of Inlier-Memorization Effect

arXiv: https://arxiv.org/abs/2408.09791 · HTML: https://mlsanity.com/paper/2408.09791/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 40 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [Importance Weighted Autoencoders](https://mlsanity.com/paper/1509.00519/) (2015)
- [Do Deep Generative Models Know What They Don't Know?](https://mlsanity.com/paper/1810.09136/) (2018)
- [Deep Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/1906.02694/) (2019)
- [Deep Anomaly Detection Using Geometric Transformations](https://mlsanity.com/paper/1805.10917/) (2018)
- [Classification-Based Anomaly Detection for General Data](https://mlsanity.com/paper/2005.02359/) (2020)
- [ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions](https://mlsanity.com/paper/2201.00382/) (2022)
- [Dash: Semi-Supervised Learning with Dynamic Thresholding](https://mlsanity.com/paper/2109.00650/) (2021)
- [COPOD: Copula-Based Outlier Detection](https://mlsanity.com/paper/2009.09463/) (2020)
- [DROCC: Deep Robust One-Class Classification](https://mlsanity.com/paper/2002.12718/) (2020)
- [Deep Structured Energy Based Models for Anomaly Detection](https://mlsanity.com/paper/1605.07717/) (2016)
- [On Diffusion Modeling for Anomaly Detection](https://mlsanity.com/paper/2305.18593/) (2023)
- [Perfect density models cannot guarantee anomaly detection](https://mlsanity.com/paper/2012.03808/) (2020)
- [ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models](https://mlsanity.com/paper/2301.04257/) (2023)

## Built on by (most cited first, 1 of 1)

- [What Drives the Inlier-Memorization Effect? A Theory of Outlier Detection via Early Training Dynamics](https://mlsanity.com/paper/2606.29791/) (2026)

## Abstract (arXiv)

Outlier detection (OD) is the task of identifying unusual observations (or outliers) from a given or upcoming data by learning unique patterns of normal observations (or inliers). Recently, a study introduced a powerful unsupervised OD (UOD) solver based on a new observation of deep generative models, called inlier-memorization (IM) effect, which suggests that generative models memorize inliers before outliers in early learning stages. In this study, we aim to develop a theoretically principled method to address UOD tasks by maximally utilizing the IM effect. We begin by observing that the IM effect is observed more clearly when the given training data contain fewer outliers. This finding indicates a potential for enhancing the IM effect in UOD regimes if we can effectively exclude outliers from mini-batches when designing the loss function. To this end, we introduce two main techniques: 1) increasing the mini-batch size as the model training proceeds and 2) using an adaptive threshold to calculate the truncated loss function. We theoretically show that these two techniques effectively filter out outliers from the truncated loss function, allowing us to utilize the IM effect to the fullest. Coupled with an additional ensemble strategy, we propose our method and term it Adaptive Loss Truncation with Batch Increment (ALTBI). We provide extensive experimental results to demonstrate that ALTBI achieves state-of-the-art performance in identifying outliers compared to other recent methods, even with significantly lower computation costs. Additionally, we show that our method yields robust performances when combined with privacy-preserving algorithms.