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Forte : Finding Outliers with Representation Typicality Estimation

Debargha Ganguly, Warren Morningstar, Andrew Yu, Vipin Chaudhary

TL;DR

The paper tackles out-of-distribution and synthetic data detection for foundation models by moving beyond likelihood-based detectors to representation-based methods that capture semantic structure. It introduces Forte, a framework that fuses self-supervised encoders (CLIP, ViT-MSN, DINOv2) with per-point summary statistics (precision, recall, density, coverage) and non-parametric density estimation to identify atypical samples. The authors provide theoretical intuition and extensive empirical evaluation on synthetic data and medical imaging tasks, showing that Forte outperforms both unsupervised and supervised baselines and highlighting the limitations of distribution-level tests. The approach is model-agnostic and scalable, offering a practical tool for robust deployment of foundation models in real-world settings.

Abstract

Generative models can now produce photorealistic synthetic data which is virtually indistinguishable from the real data used to train it. This is a significant evolution over previous models which could produce reasonable facsimiles of the training data, but ones which could be visually distinguished from the training data by human evaluation. Recent work on OOD detection has raised doubts that generative model likelihoods are optimal OOD detectors due to issues involving likelihood misestimation, entropy in the generative process, and typicality. We speculate that generative OOD detectors also failed because their models focused on the pixels rather than the semantic content of the data, leading to failures in near-OOD cases where the pixels may be similar but the information content is significantly different. We hypothesize that estimating typical sets using self-supervised learners leads to better OOD detectors. We introduce a novel approach that leverages representation learning, and informative summary statistics based on manifold estimation, to address all of the aforementioned issues. Our method outperforms other unsupervised approaches and achieves state-of-the art performance on well-established challenging benchmarks, and new synthetic data detection tasks.

Forte : Finding Outliers with Representation Typicality Estimation

TL;DR

The paper tackles out-of-distribution and synthetic data detection for foundation models by moving beyond likelihood-based detectors to representation-based methods that capture semantic structure. It introduces Forte, a framework that fuses self-supervised encoders (CLIP, ViT-MSN, DINOv2) with per-point summary statistics (precision, recall, density, coverage) and non-parametric density estimation to identify atypical samples. The authors provide theoretical intuition and extensive empirical evaluation on synthetic data and medical imaging tasks, showing that Forte outperforms both unsupervised and supervised baselines and highlighting the limitations of distribution-level tests. The approach is model-agnostic and scalable, offering a practical tool for robust deployment of foundation models in real-world settings.

Abstract

Generative models can now produce photorealistic synthetic data which is virtually indistinguishable from the real data used to train it. This is a significant evolution over previous models which could produce reasonable facsimiles of the training data, but ones which could be visually distinguished from the training data by human evaluation. Recent work on OOD detection has raised doubts that generative model likelihoods are optimal OOD detectors due to issues involving likelihood misestimation, entropy in the generative process, and typicality. We speculate that generative OOD detectors also failed because their models focused on the pixels rather than the semantic content of the data, leading to failures in near-OOD cases where the pixels may be similar but the information content is significantly different. We hypothesize that estimating typical sets using self-supervised learners leads to better OOD detectors. We introduce a novel approach that leverages representation learning, and informative summary statistics based on manifold estimation, to address all of the aforementioned issues. Our method outperforms other unsupervised approaches and achieves state-of-the art performance on well-established challenging benchmarks, and new synthetic data detection tasks.
Paper Structure (24 sections, 1 theorem, 3 equations, 18 figures, 7 tables, 1 algorithm)

This paper contains 24 sections, 1 theorem, 3 equations, 18 figures, 7 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Forte: A Framework for OOD Detection Using Per-Point Metrics
  4. Problem Setup
  5. Creating Generalized Novel Summary Statistics
  6. Develop Decision Rule
  7. Experiments
  8. Baseline Performance for Synthetic Data Distributions
  9. Results
  10. Performance on Synthetic Data Detection
  11. Performance on Medical Image Datasets
  12. Discussion
  13. Conclusion
  14. Appendix: Synthetic Data Samples
  15. Appendix: Baselines Statistical Tests
  16. ...and 9 more sections

Key Result

Theorem C.1

Under the following assumptions: Using previous definitions of Per-Point PRDC Metrics: Then, the expected values and variances of these per-point PRDC metrics are:

Figures (18)

  • Figure 1: Visualization of Precision, Recall, Density, and Coverage metrics for reference and test samples in a 2D feature space, with nearest neighbour k=1. Precision is depicted by blue circles around reference samples, recall by red circles around test samples, density by solid teal circles around reference samples, and coverage by green circles around test samples.
  • Figure 2: Synthetic images generated for the class "ambulance" using various techniques. Row (a) shows real images, while rows (b) to (f) display Img2Img generated images with strength parameters 0.3, 0.5, 0.7, 0.9, and 1.0, respectively. Row (g) presents images generated using caption descriptions of the real images in row (a), and row (h) shows images generated solely based on the classname.
  • Figure 3: Synthetic images generated for the class "golden retriever" using different techniques. Row (a) shows real images, while rows (b) to (f) display Img2Img generated images with strength parameters 0.3, 0.5, 0.7, 0.9, and 1.0, respectively. Row (g) presents images generated using caption descriptions of the real images in row (a), and row (h) shows images generated solely based on the classname.
  • Figure 4: Synthetic images generated for the class "volleyball" using various techniques. Row (a) shows real images, while rows (b) and (c) display Img2Img generated images with strength parameters 0.3, 0.5 respectively. Row (d) presents images generated using caption descriptions of the real images in row (a), and row (e) shows images generated solely based on the classname. Row (e) exhibits mode collapse properties.
  • Figure 5: PCA of Golden Retriever Images in CLIP space. We can clearly observe that the distribution of real images is most diverse, whereas the "classname" based generated images are least diverse. We also see a progression of images tending towards the modes of the distribution with increasing strengths allotted to the diffusion process.
  • ...and 13 more figures

Theorems & Definitions (2)

  • Theorem C.1
  • proof