Papers › Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling

Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling

14 Nov 2019arXiv:1911.06393archive 2025-07-28

Daniel Stoller, Mi Tian, Sebastian Ewert, Simon Dixon

Convolutional neural networks (CNNs) with dilated filters such as the Wavenet or the Temporal Convolutional Network (TCN) have shown good results in a variety of sequence modelling tasks. However, efficiently modelling long-term dependencies in these sequences is still challenging. Although the receptive field of these models grows exponentially with the number of layers, computing the convolutions over very long sequences of features in each layer is time and memory-intensive, prohibiting the use of longer receptive fields in practice. To increase efficiency, we make use of the "slow feature" hypothesis stating that many features of interest are slowly varying over time. For this, we use a U-Net architecture that computes features at multiple time-scales and adapt it to our auto-regressive scenario by making convolutions causal. We apply our model ("Seq-U-Net") to a variety of tasks including language and audio generation. In comparison to TCN and Wavenet, our network consistently saves memory and computation time, with speed-ups for training and inference of over 4x in the audio generation experiment in particular, while achieving a comparable performance in all tasks.

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char_tensor f90/Seq-U-Net/char_cnn/utils.py official repository unverified MIT (permissive) · d041553de51c481f · report
data_generator f90/Seq-U-Net/copy_memory/utils.py official repository unverified MIT (permissive) · 76d3f31a0af1e2ee · report
data_generator f90/Seq-U-Net/poly_music/utils.py official repository unverified MIT (permissive) · aa5bc0c27cde7849 · report
duplicate f90/Seq-U-Net/sequnet_utils.py official repository unverified MIT (permissive) · 37fc080688b79ae5 · report
interleave f90/Seq-U-Net/sequnet_utils.py official repository unverified MIT (permissive) · 0356ec7f2f97ed82 · report
zero_interleave f90/Seq-U-Net/sequnet_utils.py official repository unverified MIT (permissive) · ac2642e4bb2b2c5a · report

Tasks

Audio GenerationLanguage ModellingMusic Modeling

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Language Modelling Penn Treebank (Character Level) Seq-U-Net Bit per Character (BPC) 1.3 #16 of 20 Archive leaderboard report
Language Modelling Penn Treebank (Character Level) Seq-U-Net Number of params 5.9M #16 of 20 Archive leaderboard report
Language Modelling Penn Treebank (Character Level) TCN Bit per Character (BPC) 1.31 #18 of 20 Archive leaderboard report
Language Modelling Penn Treebank (Character Level) TCN Number of params 5.9M #18 of 20 Archive leaderboard report
Language Modelling Penn Treebank (Word Level) Seq-U-Net Params 14.9M #42 of 43 Archive leaderboard report
Language Modelling Penn Treebank (Word Level) Seq-U-Net Test perplexity 107.95 #42 of 43 Archive leaderboard report
Language Modelling Penn Treebank (Word Level) TCN Params 14.7M #43 of 43 Archive leaderboard report
Language Modelling Penn Treebank (Word Level) TCN Test perplexity 108.47 #43 of 43 Archive leaderboard report
Music Modeling JSB Chorales TCN NLL 8.154 #8 of 10 Archive leaderboard report
Music Modeling JSB Chorales TCN Parameters 534K #8 of 10 Archive leaderboard report
Music Modeling JSB Chorales Seq-U-Net NLL 8.173 #9 of 10 Archive leaderboard report
Music Modeling JSB Chorales Seq-U-Net Parameters 522K #9 of 10 Archive leaderboard report
Music Modeling Nottingham TCN NLL 2.783 #2 of 8 Archive leaderboard report
Music Modeling Nottingham TCN Parameters 1.7M #2 of 8 Archive leaderboard report
Music Modeling Nottingham Seq-U-Net NLL 2.97 #3 of 8 Archive leaderboard report
Music Modeling Nottingham Seq-U-Net Parameters 1.7M #3 of 8 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Concatenated Skip ConnectionConvolutionDilated Causal ConvolutionMax PoolingMixture of Logistic DistributionsReLUU-NetWaveNet

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