Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks (1805.07925v3)

Abstract: Real-world image recognition is often challenged by the variability of visual styles including object textures, lighting conditions, filter effects, etc. Although these variations have been deemed to be implicitly handled by more training data and deeper networks, recent advances in image style transfer suggest that it is also possible to explicitly manipulate the style information. Extending this idea to general visual recognition problems, we present Batch-Instance Normalization (BIN) to explicitly normalize unnecessary styles from images. Considering certain style features play an essential role in discriminative tasks, BIN learns to selectively normalize only disturbing styles while preserving useful styles. The proposed normalization module is easily incorporated into existing network architectures such as Residual Networks, and surprisingly improves the recognition performance in various scenarios. Furthermore, experiments verify that BIN effectively adapts to completely different tasks like object classification and style transfer, by controlling the trade-off between preserving and removing style variations. BIN can be implemented with only a few lines of code using popular deep learning frameworks.

• The paper introduces Batch-Instance Normalization (BIN), a novel technique that adaptively combines Batch and Instance Normalization using learnable gate parameters.
• BIN improves object classification accuracy on datasets like CIFAR-10/100 and ImageNet, demonstrating its versatility across different domains and network architectures.
• This method creates more robust, generalizable networks by selectively preserving useful style information while filtering out disruptive variations for better performance in diverse applications.

Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks

The paper "Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks" introduces a novel normalization technique designed to address variability in visual styles within image recognition tasks. The authors, Hyeonseob Nam and Hyo-Eun Kim, propose Batch-Instance Normalization (BIN) as an effective means to balance the preservation and normalization of style information in neural networks. This approach aims to selectively disregard inconsequential style features while preserving critical ones for improving model performance across a range of scenarios without a significant increase in computational complexity.

Key Contributions

1. Introduction of Batch-Instance Normalization (BIN): BIN combines Batch Normalization (BN) and Instance Normalization (IN) by introducing learnable gate parameters that determine the suitability of each normalization technique per channel. This adaptive mechanism allows the model to retain only those style attributes that contribute positively to the discriminative task, while filtering out disruptive style variations.
2. Improving Recognition in Diverse Scenarios: The research demonstrates that BIN enhances object classification accuracy in datasets like CIFAR-10/100 and ImageNet when substituted for conventional BN layers. BIN addresses both general object classification and style transfer tasks, establishing its versatility and scalability across different domains and network architectures.
3. Experimental Validation Across Multiple Applications: Extensive experimentation indicates BIN's superiority over baseline methods such as BN and IN in both object classification and image style transfer. For instance, BIN surpasses BN in terms of top-1 accuracy in object classification tasks and maintains stylistic adaptiveness needed for effective image style transfer.
4. Intelligent Style Adaptation for Multi-Domain Learning: BIN's ability to mitigate styles inconsistent across various domains proves beneficial in multi-domain learning tasks. It has been shown to achieve higher classification accuracy by effectively neutralizing domain-specific style discrepancies, enabling better knowledge transfer in domain adaptation scenarios.

Implications and Future Directions

The adaptive nature of BIN has significant implications for the development of more robust, generalizable neural networks. By tailoring style management to specific dataset requirements, BIN offers a promising solution to the problem of style variability, which continues to hinder models' applicability to real-world tasks. Future research could benefit from exploring the integration of BIN with other normalization strategies or elaborating on task-specific tuning of style gate parameters to optimize performance further. Additionally, examining the potential implementation of BIN within emerging architectures such as Transformers and Vision Transformers could provide valuable insights into its applicability across various machine learning paradigms.

In conclusion, the proposed BIN technique presents a constructive advancement in managing style variability, a common challenge in visual recognition. It elegantly bridges the strengths of BN and IN, catering to the evolving demands of neural networks in an increasingly diverse range of applications. The ability of BIN to seamlessly integrate into existing architectures without substantial overhead suggests its relevance as a practical and efficient solution to the complexities introduced by varying style information.