379 research outputs found
Improving novelty detection using the reconstructions of nearest neighbours
We show that using nearest neighbours in the latent space of autoencoders
(AE) significantly improves performance of semi-supervised novelty detection in
both single and multi-class contexts. Autoencoding methods detect novelty by
learning to differentiate between the non-novel training class(es) and all
other unseen classes. Our method harnesses a combination of the reconstructions
of the nearest neighbours and the latent-neighbour distances of a given input's
latent representation. We demonstrate that our nearest-latent-neighbours (NLN)
algorithm is memory and time efficient, does not require significant data
augmentation, nor is reliant on pre-trained networks. Furthermore, we show that
the NLN-algorithm is easily applicable to multiple datasets without
modification. Additionally, the proposed algorithm is agnostic to autoencoder
architecture and reconstruction error method. We validate our method across
several standard datasets for a variety of different autoencoding architectures
such as vanilla, adversarial and variational autoencoders using either
reconstruction, residual or feature consistent losses. The results show that
the NLN algorithm grants up to a 17% increase in Area Under the Receiver
Operating Characteristics (AUROC) curve performance for the multi-class case
and 8% for single-class novelty detection
Triggering Dark Showers with Conditional Dual Auto-Encoders
Auto-encoders (AEs) have the potential to be effective and generic tools for
new physics searches at colliders, requiring little to no model-dependent
assumptions. New hypothetical physics signals can be considered anomalies that
deviate from the well-known background processes generally expected to describe
the whole dataset. We present a search formulated as an anomaly detection (AD)
problem, using an AE to define a criterion to decide about the physics nature
of an event. In this work, we perform an AD search for manifestations of a dark
version of strong force using raw detector images, which are large and very
sparse, without leveraging any physics-based pre-processing or assumption on
the signals. We propose a dual-encoder design which can learn a compact latent
space through conditioning. In the context of multiple AD metrics, we present a
clear improvement over competitive baselines and prior approaches. It is the
first time that an AE is shown to exhibit excellent discrimination against
multiple dark shower models, illustrating the suitability of this method as a
performant, model-independent algorithm to deploy, e.g., in the trigger stage
of LHC experiments such as ATLAS and CMS.Comment: 25 pages, 7 figures, and 11 table
Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models
Morphed face images have recently become a growing concern for existing face
verification systems, as they are relatively easy to generate and can be used
to impersonate someone's identity for various malicious purposes. Efficient
Morphing Attack Detection (MAD) that generalizes well across different morphing
techniques is, therefore, of paramount importance. Existing MAD techniques
predominantly rely on discriminative models that learn from examples of bona
fide and morphed images and, as a result, often exhibit sub-optimal
generalization performance when confronted with unknown types of morphing
attacks. To address this problem, we propose a novel, diffusion-based MAD
method in this paper that learns only from the characteristics of bona fide
images. Various forms of morphing attacks are then detected by our model as
out-of-distribution samples. We perform rigorous experiments over four
different datasets (CASIA-WebFace, FRLL-Morphs, FERET-Morphs and FRGC-Morphs)
and compare the proposed solution to both discriminatively-trained and
once-class MAD models. The experimental results show that our MAD model
achieves highly competitive results on all considered datasets.Comment: Published at IWBF 202
Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images
Medical anomaly detection is a crucial yet challenging task aiming at
recognizing abnormal images to assist diagnosis. Due to the high-cost
annotations of abnormal images, most methods utilize only known normal images
during training and identify samples not conforming to the normal profile as
anomalies in the testing phase. A large number of readily available unlabeled
images containing anomalies are thus ignored in the training phase, restricting
their performance. To solve this problem, we propose the Dual-distribution
Discrepancy for Anomaly Detection (DDAD), utilizing both known normal images
and unlabeled images. Two modules are designed to model the normative
distribution of normal images and the unknown distribution of both normal and
unlabeled images, respectively, using ensembles of reconstruction networks.
Subsequently, intra-discrepancy of the normative distribution module, and
inter-discrepancy between the two modules are designed as anomaly scores.
Furthermore, an Anormal Score Refinement Net (ASR-Net) trained via
self-supervised learning is proposed to refine the two anomaly scores. For
evaluation, five medical datasets including chest X-rays, brain MRIs and
retinal fundus images are organized as benchmarks. Experiments on these
benchmarks demonstrate our method achieves significant gains and outperforms
state-of-the-art methods. Code and organized benchmarks will be available at
https://github.com/caiyu6666/DDAD-ASRComment: Under consideration. arXiv admin note: text overlap with
arXiv:2206.0393
Inverting Adversarially Robust Networks for Image Synthesis
Recent research in adversarially robust classifiers suggests their
representations tend to be aligned with human perception, which makes them
attractive for image synthesis and restoration applications. Despite favorable
empirical results on a few downstream tasks, their advantages are limited to
slow and sensitive optimization-based techniques. Moreover, their use on
generative models remains unexplored. This work proposes the use of robust
representations as a perceptual primitive for feature inversion models, and
show its benefits with respect to standard non-robust image features. We
empirically show that adopting robust representations as an image prior
significantly improves the reconstruction accuracy of CNN-based feature
inversion models. Furthermore, it allows reconstructing images at multiple
scales out-of-the-box. Following these findings, we propose an
encoding-decoding network based on robust representations and show its
advantages for applications such as anomaly detection, style transfer and image
denoising
CVAD: A generic medical anomaly detector based on Cascade VAE
Detecting out-of-distribution (OOD) samples in medical imaging plays an important role for downstream medical diagnosis. However, existing OOD detectors are demonstrated on natural images composed of inter-classes and have difficulty generalizing to medical images. The key issue is the granularity of OOD data in the medical domain, where intra-class OOD samples are predominant. We focus on the generalizability of OOD detection for medical images and propose a self-supervised Cascade Variational autoencoder-based Anomaly Detector (CVAD). We use a variational autoencoders' cascade architecture, which combines latent representation at multiple scales, before being fed to a discriminator to distinguish the OOD data from the in-distribution (ID) data. Finally, both the reconstruction error and the OOD probability predicted by the binary discriminator are used to determine the anomalies. We compare the performance with the state-of-the-art deep learning models to demonstrate our model's efficacy on various open-access medical imaging datasets for both intra- and inter-class OOD. Further extensive results on datasets including common natural datasets show our model's effectiveness and generalizability
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