Time-Series Few-Shot Learning with Heterogeneous Channels

Benchmarks archive 2025-07-28

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Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

5 shown of 5 papers with code (5 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Generative Adversarial Networks
  10 Jun 2014 189 repositories listed Syntology ran 23 of 55 samples · 32 unverified · 18 pointer-only (licence)
  We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D…
• N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
  24 May 2019 18 repositories listed Syntology ran 10 of 39 samples · 29 unverified · 3 pointer-only (licence)
  We focus on solving the univariate times series point forecasting problem using deep learning.
• Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline
  20 Nov 2016 12 repositories listed Syntology ran 1 of 10 samples · 9 unverified · 1 pointer-only (licence)
  We propose a simple but strong baseline for time series classification from scratch with deep neural networks.
• Few-Shot Forecasting of Time-Series with Heterogeneous Channels
  7 Apr 2022 1 repository listed
  Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set.
• Meta-learning from Tasks with Heterogeneous Attribute Spaces
  1 Dec 2020 1 repository listed
  We propose a heterogeneous meta-learning method that trains a model on tasks with various attribute spaces, such that it can solve unseen tasks whose attribute spaces are different from the training tasks given a few…

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.