Unsupervised detection of mouse behavioural anomalies using two-stream convolutional autoencoders

• URL: http://arxiv.org/abs/2106.00598v1
• Date: Fri, 28 May 2021 16:30:09 GMT
• Title: Unsupervised detection of mouse behavioural anomalies using two-stream convolutional autoencoders
• Authors: Ezechukwu I Nwokedi, Rasneer S Bains, Luc Bidaut, Sara Wells, Xujiong Ye, James M Brown
• Abstract summary: This paper explores the application of unsupervised learning to detecting anomalies in mouse video data. The two models presented are a dual-stream, 3D convolutional autoencoder and a dual-stream, 2D convolutional autoencoder.
• Score: 1.1236899956615454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores the application of unsupervised learning to detecting anomalies in mouse video data. The two models presented in this paper are a dual-stream, 3D convolutional autoencoder (with residual connections) and a dual-stream, 2D convolutional autoencoder. The publicly available dataset used here contains twelve videos of single home-caged mice alongside frame-level annotations. Under the pretext that the autoencoder only sees normal events, the video data was handcrafted to treat each behaviour as a pseudo-anomaly thereby eliminating them from the others during training. The results are presented for one conspicuous behaviour (hang) and one inconspicuous behaviour (groom). The performance of these models is compared to a single stream autoencoder and a supervised learning model, which are both based on the custom CAE. Both models are also tested on the CUHK Avenue dataset were found to perform as well as some state-of-the-art architectures.

Related papers

• Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion Representations [17.816344808780965]
  unsupervised video anomaly detection (VAD) problem involves classifying each frame in a video as normal or abnormal, without any access to labels. To accomplish this, proposed method employs conditional diffusion models, where the input data is features extracted from pre-trained network. Our method utilizes a data-driven threshold and considers a high reconstruction error as an indicator of anomalous events.
  arXiv  Detail & Related papers  (2023-07-04T07:36:48Z)
• Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors [117.61449210940955]
  We propose an efficient abnormal event detection model based on a lightweight masked auto-encoder (AE) applied at the video frame level. We introduce an approach to weight tokens based on motion gradients, thus shifting the focus from the static background scene to the foreground objects. We generate synthetic abnormal events to augment the training videos, and task the masked AE model to jointly reconstruct the original frames.
  arXiv  Detail & Related papers  (2023-06-21T06:18:05Z)
• Dual Learning for Large Vocabulary On-Device ASR [64.10124092250128]
  Dual learning is a paradigm for semi-supervised machine learning that seeks to leverage unsupervised data by solving two opposite tasks at once. We provide an analysis of an on-device-sized streaming conformer trained on the entirety of Librispeech, showing relative WER improvements of 10.7%/5.2% without an LM and 11.7%/16.4% with an LM.
  arXiv  Detail & Related papers  (2023-01-11T06:32:28Z)
• Visual anomaly detection in video by variational autoencoder [0.0]
  An autoencoder is a neural network that is trained to recreate its input using latent representation of input also called a bottleneck layer. In this paper we have demonstrated comparison between performance of convolutional LSTM versus a variation convolutional LSTM autoencoder.
  arXiv  Detail & Related papers  (2022-03-08T06:22:04Z)
• Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection [0.0]
  We propose a novel reconstruction-based model for anomaly detection, called Y-GAN. The model consists of a Y-shaped auto-encoder and represents images in two separate latent spaces.
  arXiv  Detail & Related papers  (2021-09-28T20:17:04Z)
• ASCNet: Self-supervised Video Representation Learning with Appearance-Speed Consistency [62.38914747727636]
  We study self-supervised video representation learning, which is a challenging task due to 1) a lack of labels for explicit supervision and 2) unstructured and noisy visual information. Existing methods mainly use contrastive loss with video clips as the instances and learn visual representation by discriminating instances from each other. In this paper, we observe that the consistency between positive samples is the key to learn robust video representations.
  arXiv  Detail & Related papers  (2021-06-04T08:44:50Z)
• Autoencoding Variational Autoencoder [56.05008520271406]
  We study the implications of this behaviour on the learned representations and also the consequences of fixing it by introducing a notion of self consistency. We show that encoders trained with our self-consistency approach lead to representations that are robust (insensitive) to perturbations in the input introduced by adversarial attacks.
  arXiv  Detail & Related papers  (2020-12-07T14:16:14Z)
• Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features [8.407188666535506]
  Most existing methods use an autoencoder to learn to reconstruct normal videos. We propose an implicit two-path AE (ITAE), a structure in which two encoders implicitly model appearance and motion features. For the complex distribution of normal scenes, we suggest normal density estimation of ITAE features. NF models intensify ITAE performance by learning normality through implicitly learned features.
  arXiv  Detail & Related papers  (2020-10-15T05:02:02Z)
• Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection [54.98042023365694]
  We propose a noise-aware encoder-decoder framework to disentangle a clean saliency predictor from noisy training examples. The proposed model consists of two sub-models parameterized by neural networks.
  arXiv  Detail & Related papers  (2020-07-23T18:47:36Z)
• EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement [53.69674636044927]
  We present EHSOD, an end-to-end hybrid-supervised object detection system. It can be trained in one shot on both fully and weakly-annotated data. It achieves comparable results on multiple object detection benchmarks with only 30% fully-annotated data.
  arXiv  Detail & Related papers  (2020-02-18T08:04:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.