Multiple Distribution Shift -- Aerial (MDS-A): A Dataset for Test-Time Error Detection and Model Adaptation

• URL: http://arxiv.org/abs/2502.13289v1
• Date: Tue, 18 Feb 2025 21:17:03 GMT
• Title: Multiple Distribution Shift -- Aerial (MDS-A): A Dataset for Test-Time Error Detection and Model Adaptation
• Authors: Noel Ngu, Aditya Taparia, Gerardo I. Simari, Mario Leiva, Jack Corcoran, Ransalu Senanayake, Paulo Shakarian, Nathaniel D. Bastian,
• Abstract summary: Multiple Distribution Shift -- Aerial (MDS-A) is a collection of inter-related datasets of the same aerial domain. We present characterizations of MDS-A, provide performance results for the baseline machine learning models.
• Score: 10.83324226483804
• License:
• Abstract: Machine learning models assume that training and test samples are drawn from the same distribution. As such, significant differences between training and test distributions often lead to degradations in performance. We introduce Multiple Distribution Shift -- Aerial (MDS-A) -- a collection of inter-related datasets of the same aerial domain that are perturbed in different ways to better characterize the effects of out-of-distribution performance. Specifically, MDS-A is a set of simulated aerial datasets collected under different weather conditions. We include six datasets under different simulated weather conditions along with six baseline object-detection models, as well as several test datasets that are a mix of weather conditions that we show have significant differences from the training data. In this paper, we present characterizations of MDS-A, provide performance results for the baseline machine learning models (on both their specific training datasets and the test data), as well as results of the baselines after employing recent knowledge-engineering error-detection techniques (EDR) thought to improve out-of-distribution performance. The dataset is available at https://lab-v2.github.io/mdsa-dataset-website.

Related papers

• Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection [10.12283550685127]
  We propose an Adapted-MoE to handle multiple distributions of same-category samples by divide and conquer. Specifically, we propose a routing network based on representation learning to route same-category samples into the subclasses feature space. We propose the test-time adaption to eliminate the bias between the unseen test sample representation and the feature distribution learned by the expert model.
  arXiv  Detail & Related papers  (2024-09-09T13:49:09Z)
• MDM: Advancing Multi-Domain Distribution Matching for Automatic Modulation Recognition Dataset Synthesis [35.07663680944459]
  Deep learning technology has been successfully introduced into Automatic Modulation Recognition (AMR) tasks. The success of deep learning is all attributed to the training on large-scale datasets. In order to solve the problem of large amount of data, some researchers put forward the method of data distillation.
  arXiv  Detail & Related papers  (2024-08-05T14:16:54Z)
• Task-customized Masked AutoEncoder via Mixture of Cluster-conditional Experts [104.9871176044644]
  Masked Autoencoder(MAE) is a prevailing self-supervised learning method that achieves promising results in model pre-training. We propose a novel MAE-based pre-training paradigm, Mixture of Cluster-conditional Experts (MoCE) MoCE trains each expert only with semantically relevant images by using cluster-conditional gates.
  arXiv  Detail & Related papers  (2024-02-08T03:46:32Z)
• The SVHN Dataset Is Deceptive for Probabilistic Generative Models Due to a Distribution Mismatch [12.542073306638988]
  The Street View House Numbers dataset is a popular benchmark dataset in deep learning. We warn that the official split into training set and test set of the SVHN dataset are not drawn from the same distribution. We propose to mix and re-split the official training and test set when SVHN is used for tasks other than classification.
  arXiv  Detail & Related papers  (2023-10-30T15:38:31Z)
• Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction [2.921945366485149]
  Industrial anomaly detection (IAD) is crucial for automating industrial quality inspection. Existing IAD datasets focus on the diversity of data categories. We propose the Aero-engine Blade Anomaly Detection (AeBAD) dataset, consisting of two sub-datasets.
  arXiv  Detail & Related papers  (2023-04-05T04:07:54Z)
• Quantifying the LiDAR Sim-to-Real Domain Shift: A Detailed Investigation Using Object Detectors and Analyzing Point Clouds at Target-Level [1.1999555634662635]
  LiDAR object detection algorithms based on neural networks for autonomous driving require large amounts of data for training, validation, and testing. We show that using simulated data for the training of neural networks leads to a domain shift of training and testing data due to differences in scenes, scenarios, and distributions.
  arXiv  Detail & Related papers  (2023-03-03T12:52:01Z)
• CAFA: Class-Aware Feature Alignment for Test-Time Adaptation [50.26963784271912]
  Test-time adaptation (TTA) aims to address this challenge by adapting a model to unlabeled data at test time. We propose a simple yet effective feature alignment loss, termed as Class-Aware Feature Alignment (CAFA), which simultaneously encourages a model to learn target representations in a class-discriminative manner.
  arXiv  Detail & Related papers  (2022-06-01T03:02:07Z)
• CHALLENGER: Training with Attribution Maps [63.736435657236505]
  We show that utilizing attribution maps for training neural networks can improve regularization of models and thus increase performance. In particular, we show that our generic domain-independent approach yields state-of-the-art results in vision, natural language processing and on time series tasks.
  arXiv  Detail & Related papers  (2022-05-30T13:34:46Z)
• Listen, Adapt, Better WER: Source-free Single-utterance Test-time Adaptation for Automatic Speech Recognition [65.84978547406753]
  Test-time Adaptation aims to adapt the model trained on source domains to yield better predictions for test samples. Single-Utterance Test-time Adaptation (SUTA) is the first TTA study in speech area to our best knowledge.
  arXiv  Detail & Related papers  (2022-03-27T06:38:39Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Comparing Test Sets with Item Response Theory [53.755064720563]
  We evaluate 29 datasets using predictions from 18 pretrained Transformer models on individual test examples. We find that Quoref, HellaSwag, and MC-TACO are best suited for distinguishing among state-of-the-art models. We also observe span selection task format, which is used for QA datasets like QAMR or SQuAD2.0, is effective in differentiating between strong and weak models.
  arXiv  Detail & Related papers  (2021-06-01T22:33:53Z)

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.