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Conditional Batch Normalization

145 papers tagged archive 2025-07-28

Introduced by Harm de Vries et al. in Modulating early visual processing by language

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

Conditional Batch Normalization (CBN) is a class-conditional variant of batch normalization. The key idea is to predict the γ and β of the batch normalization from an embedding - e.g. a language embedding in VQA. CBN enables the linguistic embedding to manipulate entire feature maps by scaling them up or down, negating them, or shutting them off. CBN has also been used in GANs to allow class information to affect the batch normalization parameters.

Consider a single convolutional layer with batch normalization module BN(F_(i,c,h,w)|γ_c, β_c) for which pretrained scalars γ_c and β_c are available. We would like to directly predict these affine scaling parameters from, e.g., a language embedding 𝐞_𝐪. When starting the training procedure, these parameters must be close to the pretrained values to recover the original ResNet model as a poor initialization could significantly deteriorate performance. Unfortunately, it is difficult to initialize a network to output the pretrained γ and β. For these reasons, the authors propose to predict a change δβ_c and δγ_c on the frozen original scalars, for which it is straightforward to initialize a neural network to produce an output with zero-mean and small variance.

The authors use a one-hidden-layer MLP to predict these deltas from a question embedding 𝐞_𝐪 for all feature maps within the layer:

Δβ= MLP(𝐞_𝐪)

Δγ= MLP(𝐞_𝐪)

So, given a feature map with C channels, these MLPs output a vector of size C. We then add these predictions to the β and γ parameters:

β̂_c = β_c + Δβ_c

γ̂_c = γ_c + Δγ_c

Finally, these updated β̂ and γ̂ are used as parameters for the batch normalization: BN(F_(i,c,h,w)|γ̂_̂ĉ, β̂_̂ĉ). The authors freeze all ResNet parameters, including γ and β, during training. A ResNet consists of four stages of computation, each subdivided in several residual blocks. In each block, the authors apply CBN to the three convolutional layers.

PaperSourceSee Code · ap229997/Conditional-Batch-Norm

Papers archive 2025-07-28

30 shown of 145, 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 145 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 Generation42
Conditional Image Generation16
Generative Adversarial Network12
reinforcement-learning9
Multi-agent Reinforcement Learning7
Reinforcement Learning7
Data Augmentation6
Reinforcement Learning (RL)6
Super-Resolution6
Vocal Bursts Intensity Prediction6
Decision Making5
Transfer Learning5
Unconditional Image Generation5
Attribute4
Clustering4
Denoising4
Diversity4
Object4
Benchmarking3
Decoder3

Usage over time archive 2025-07-28

Papers per year tagged with Conditional Batch Normalization: 2017 to 2024, peak 38 38 0 2017: 2 papers 2017 2018: 4 papers 2018 2019: 11 papers 2019 2020: 31 papers 2020 2021: 21 papers 2021 2022: 35 papers 2022 2023: 38 papers 2023 2024: 3 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (145 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

Normalization

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