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Reversible Residual Block

29 papers tagged archive 2025-07-28

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

Reversible Residual Blocks are skip-connection blocks that learn reversible residual functions with reference to the layer inputs. It is proposed as part of the RevNet CNN architecture. Units in each layer are partitioned into two groups, denoted x₁ and x₂; the authors find what works best is partitioning the channels. Each reversible block takes inputs (x₁, x₂) and produces outputs (y₁, y₂) according to the following additive coupling rules – inspired by the transformation in NICE (nonlinear independent components estimation) – and residual functions F and G analogous to those in standard ResNets:

y₁ = x₁ + F(x₂) y₂ = x₂ + G(y₁)

Each layer’s activations can be reconstructed from the next layer’s activations as follows:

x₂ = y₂ − G(y₁) x₁ = y₁ − F(x₂)

Source: The Reversible Residual Network: Backpropagation Without...See Code · osmr/imgclsmob

Papers archive 2025-07-28

29 shown of 29, 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 74 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
Image Classification3
Image Generation3
image-classification3
Attribute2
Deep Learning2
Image Restoration2
Language Modelling2
Machine Translation2
Reinforcement Learning (RL)2
Segmentation2
Sentence2
Survey2
Time Series2
Time Series Analysis2
Time Series Forecasting2
Translation2
BIG-bench Machine Learning1
Binary Classification1
Classification1
Click-Through Rate Prediction1

Usage over time archive 2025-07-28

Papers per year tagged with Reversible Residual Block: 2017 to 2025, peak 8 8 0 2017: 2 papers 2017 2018: 0 papers 2018 2019: 1 paper 2019 2020: 8 papers 2020 2021: 6 papers 2021 2022: 5 papers 2022 2023: 2 papers 2023 2024: 3 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (29 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

Skip Connection Blocks

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