LAND: A Longitudinal Analysis of Neuromorphic Datasets

• URL: http://arxiv.org/abs/2602.15973v1
• Date: Tue, 17 Feb 2026 19:46:33 GMT
• Title: LAND: A Longitudinal Analysis of Neuromorphic Datasets
• Authors: Gregory Cohen, Alexandre Marcireau,
• Abstract summary: This review captures the existing neuromorphic datasets, covering over 423 datasets.<n>Analysing these datasets shows the difficulties arising from their size, the lack of standardisation, and difficulties in accessing the actual data.<n>A more important concern is the rise of synthetic datasets, created by either simulation or video-to-events methods.
• Score: 47.84658158364453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neuromorphic engineering has a data problem. Despite the meteoric rise in the number of neuromorphic datasets published over the past ten years, the conclusion of a significant portion of neuromorphic research papers still states that there is a need for yet more data and even larger datasets. Whilst this need is driven in part by the sheer volume of data required by modern deep learning approaches, it is also fuelled by the current state of the available neuromorphic datasets and the difficulties in finding them, understanding their purpose, and determining the nature of their underlying task. This is further compounded by practical difficulties in downloading and using these datasets. This review starts by capturing a snapshot of the existing neuromorphic datasets, covering over 423 datasets, and then explores the nature of their tasks and the underlying structure of the presented data. Analysing these datasets shows the difficulties arising from their size, the lack of standardisation, and difficulties in accessing the actual data. This paper also highlights the growth in the size of individual datasets and the complexities involved in working with the data. However, a more important concern is the rise of synthetic datasets, created by either simulation or video-to-events methods. This review explores the benefits of simulated data for testing existing algorithms and applications, highlighting the potential pitfalls for exploring new applications of neuromorphic technologies. This review also introduces the concepts of meta-datasets, created from existing datasets, as a way of both reducing the need for more data, and to remove potential bias arising from defining both the dataset and the task.

Related papers

• Synthetic Tabular Data: Methods, Attacks and Defenses [12.374541748245843]
  Synthetic data is often positioned as a solution to replace sensitive fixed-size datasets with a source of unlimited matching data, freed from privacy concerns.<n>There has been much progress in synthetic data generation over the last decade, leveraging corresponding advances in machine learning and data analytics.
  arXiv  Detail & Related papers  (2025-06-06T14:16:57Z)
• Causal-StoNet: Causal Inference for High-Dimensional Complex Data [7.648784748888187]
  This paper proposes a novel causal inference approach for dealing with high-dimensional complex data. It is based on deep learning techniques, including sparse deep learning theory and neural networks. The proposed approach can also be used when missing values are present in the datasets.
  arXiv  Detail & Related papers  (2024-03-27T20:27:31Z)
• Exploring the Potential of AI-Generated Synthetic Datasets: A Case Study on Telematics Data with ChatGPT [0.0]
  This research delves into the construction and utilization of synthetic datasets, specifically within the telematics sphere, leveraging OpenAI's powerful language model, ChatGPT. To illustrate this data creation process, a hands-on case study is conducted, focusing on the generation of a synthetic telematics dataset.
  arXiv  Detail & Related papers  (2023-06-23T15:15:13Z)
• LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting [65.71129509623587]
  Road traffic forecasting plays a critical role in smart city initiatives and has experienced significant advancements thanks to the power of deep learning. However, the promising results achieved on current public datasets may not be applicable to practical scenarios. We introduce the LargeST benchmark dataset, which includes a total of 8,600 sensors in California with a 5-year time coverage.
  arXiv  Detail & Related papers  (2023-06-14T05:48:36Z)
• Dataset Distillation: A Comprehensive Review [76.26276286545284]
  dataset distillation (DD) aims to derive a much smaller dataset containing synthetic samples, based on which the trained models yield performance comparable with those trained on the original dataset. This paper gives a comprehensive review and summary of recent advances in DD and its application.
  arXiv  Detail & Related papers  (2023-01-17T17:03:28Z)
• A Comprehensive Survey of Dataset Distillation [73.15482472726555]
  It has become challenging to handle the unlimited growth of data with limited computing power. Deep learning technology has developed unprecedentedly in the last decade. This paper provides a holistic understanding of dataset distillation from multiple aspects.
  arXiv  Detail & Related papers  (2023-01-13T15:11:38Z)
• A Survey of Dataset Refinement for Problems in Computer Vision Datasets [11.45536223418548]
  Large-scale datasets have played a crucial role in the advancement of computer vision. They often suffer from problems such as class imbalance, noisy labels, dataset bias, or high resource costs. Various data-centric solutions have been proposed to solve the dataset problems. They improve the quality of datasets by re-organizing them, which we call dataset refinement.
  arXiv  Detail & Related papers  (2022-10-21T03:58:43Z)
• TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments [84.6017003787244]
  This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets. We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
  arXiv  Detail & Related papers  (2022-08-16T20:46:08Z)
• DataPerf: Benchmarks for Data-Centric AI Development [81.03754002516862]
  DataPerf is a community-led benchmark suite for evaluating ML datasets and data-centric algorithms. We provide an open, online platform with multiple rounds of challenges to support this iterative development. The benchmarks, online evaluation platform, and baseline implementations are open source.
  arXiv  Detail & Related papers  (2022-07-20T17:47:54Z)
• On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Models [0.0]
  High-quality labeled datasets play a crucial role in fueling the development of machine learning (ML) Since the emergence of the ImageNet dataset and the AlexNet model in 2012, the size of new open-source labeled vision datasets has remained roughly constant. Only a minority of publications in the computer vision community tackle supervised learning on datasets that are orders of magnitude larger than Imagenet.
  arXiv  Detail & Related papers  (2021-07-31T00:08:21Z)
• DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier [58.979104709647295]
  We bridge the gap between the abundance of available data and lack of relevant data, for the future learning tasks of a trained network. We use the available data, that may be an imbalanced subset of the original training dataset, or a related domain dataset, to retrieve representative samples. We demonstrate that data from a related domain can be leveraged to achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2019-12-27T02:05:45Z)

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.