MetaNeXt is a meta-architecture abstracted from ConvNeXt, which can be regarded as a simpler version obtained from MetaFormer by merging token mixer and MLP into one.

This package focuses on the time delay estimation between two event-based time-series that are relatively shifted by an unknown time offset. An event-based time-series is given by a set of timestamps of certain events. If you want to guarantee synchronous measurements in advance or estimate the time delay of continuous measurements sampled at a constant rate, you might want to use other methods. However, in some use cases, performing an event detection and then estimating the relative time delay has advantages. **The Nearest Advocate method provides a precise time delay estimation in event-based time-series that is robust against imprecise timestamps, a high fraction of missing events, and clock drift.** Time delay estimation also known as the correction of time offsets and time lags as well as time synchronization.

NearestAdvocate

Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronization

MetaNeXt

InceptionNeXt: When Inception Meets ConvNeXt

While the typical approach to leverage transfer learning in image segmentation models involves replacing the entire encoder, this can restrict the customization and unique strengths of the main network. To address this limitation, we propose a hybrid transfer learning strategy that incorporates pre-trained convolutional layers from two distinct architectures (e.g., VGG16 and DenseNet201), allowing us to leverage the advantages of transfer learning while preserving the core features and characteristics of the base model's architecture.

Source	InceptionNeXt: When Inception Meets ConvNeXt
Year
Data Source	Papers with Code archive (CC BY-SA)