Adaptative Context Normalization: A Boost for Deep Learning in Image Processing
This work addresses a key bottleneck in deep learning for image processing, offering an incremental improvement over existing normalization methods.
The paper tackles the challenge of distribution shifts in deep neural networks for image processing by introducing Adaptative Context Normalization (ACN), a supervised method that groups data into contexts for normalization, resulting in faster convergence and superior performance compared to Batch Normalization and Mixture Normalization.
Deep Neural network learning for image processing faces major challenges related to changes in distribution across layers, which disrupt model convergence and performance. Activation normalization methods, such as Batch Normalization (BN), have revolutionized this field, but they rely on the simplified assumption that data distribution can be modelled by a single Gaussian distribution. To overcome these limitations, Mixture Normalization (MN) introduced an approach based on a Gaussian Mixture Model (GMM), assuming multiple components to model the data. However, this method entails substantial computational requirements associated with the use of Expectation-Maximization algorithm to estimate parameters of each Gaussian components. To address this issue, we introduce Adaptative Context Normalization (ACN), a novel supervised approach that introduces the concept of "context", which groups together a set of data with similar characteristics. Data belonging to the same context are normalized using the same parameters, enabling local representation based on contexts. For each context, the normalized parameters, as the model weights are learned during the backpropagation phase. ACN not only ensures speed, convergence, and superior performance compared to BN and MN but also presents a fresh perspective that underscores its particular efficacy in the field of image processing.