Methods › Sequential › Recurrent Neural Networks › Residual GRU

Residual GRU

66 papers tagged archive 2025-07-28

Introduced by George Toderici et al. in Full Resolution Image Compression with Recurrent Neural Networks

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

A Residual GRU is a gated recurrent unit (GRU) that incorporates the idea of residual connections from ResNets.

PaperSource

Papers archive 2025-07-28

30 shown of 66, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 50 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Speech Synthesis43
Text to Speech41
text-to-speech41
Text-To-Speech Synthesis15
Decoder11
Sentence6
Transfer Learning5
Voice Cloning5
Speech Recognition4
Voice Conversion4
Audio Synthesis3
Expressive Speech Synthesis3
GPU3
speech-recognition3
All2
CPU2
Data Augmentation2
Diversity2
Generative Adversarial Network2
Self-Supervised Learning2

Usage over time archive 2025-07-28

Papers per year tagged with Residual GRU: 2016 to 2024, peak 16 16 0 2016: 1 paper 2016 2017: 4 papers 2017 2018: 6 papers 2018 2019: 7 papers 2019 2020: 16 papers 2020 2021: 13 papers 2021 2022: 9 papers 2022 2023: 6 papers 2023 2024: 4 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (66 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Recurrent Neural Networks

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