Domain Adaptive Neural Networks for Object Recognition (1409.6041v1)

Abstract: We propose a simple neural network model to deal with the domain adaptation problem in object recognition. Our model incorporates the Maximum Mean Discrepancy (MMD) measure as a regularization in the supervised learning to reduce the distribution mismatch between the source and target domains in the latent space. From experiments, we demonstrate that the MMD regularization is an effective tool to provide good domain adaptation models on both SURF features and raw image pixels of a particular image data set. We also show that our proposed model, preceded by the denoising auto-encoder pretraining, achieves better performance than recent benchmark models on the same data sets. This work represents the first study of MMD measure in the context of neural networks.

• The paper introduces MMD regularization in neural networks to bridge feature gaps between source and target domains.
• It demonstrates that Denoising Auto-Encoder pretraining builds robust representations when labeled data is scarce.
• Empirical results on the Office dataset show that the proposed approach outperforms traditional domain adaptation methods.

Domain Adaptive Neural Networks for Object Recognition: A Technical Overview

Object recognition models often face significant difficulties when applied to environments that differ from those encountered during their training phase. This problem, broadly referred to as domain adaptation, is particularly challenging due to the potential mismatches in the probability distributions governing different domains. In the paper "Domain Adaptive Neural Networks for Object Recognition," the authors present a neural network model that incorporates domain adaptation techniques to reduce these distribution mismatches, thereby improving the robustness of object recognition systems.

A central innovation in this work is the integration of the Maximum Mean Discrepancy (MMD) as a regularization term in the supervised training of neural networks. MMD is a statistical measure used to quantify the difference between two probability distributions. By leveraging this measure, the authors aim to minimize the differences in feature distributions between the source and target domains within the hidden layers of the network, thereby facilitating better adaptation across domains.

Key Findings and Contributions

1. MMD Regularization in Neural Networks: The paper introduces the novel concept of incorporating MMD as a regularization term directly into neural network training. This approach is designed to reduce distribution mismatch between source and target domain data in the hidden layer representations, emphasizing its novel application to neural networks for the first time.
2. Denoising Auto-Encoder Pretraining: The paper also evaluates the efficacy of Denoising Auto-Encoder (DAE) pretraining as a precursor to the main training phase. DAE is used to build robust intermediate representations without reliance on labeled data, which is particularly useful in addressing scarcity in labeled target domain data.
3. Empirical Evaluation: Using the Office dataset, consisting of images from three different domains (amazon, webcam, dslr), the authors compare the proposed Domain Adaptive Neural Network (DaNN) with various baselines and domain adaptation models, including Transfer Sparse Coding (TSC) and Geodesic Flow Kernel (GFK). The results indicate that DaNN, particularly when combined with DAE pretraining, consistently outperforms other methods on raw image pixel inputs, achieving significant improvements on average recognition accuracy.
4. Reduced Dependence on Handcrafted Features: By demonstrating competitive performance using raw pixel data, the paper presents evidence that effective object recognition models can be trained without heavy reliance on handcrafted features, which often require exhaustive preprocessing and domain-specific knowledge.
5. Scalability and Efficiency: While the focus is largely on efficacy, the proposed model’s reliance on straightforward feed-forward architectures with regularization bolsters confidence in its scalability and efficiency for real-world applications where real-time processing is crucial.

Implications and Future Work

The integration of MMD as a regularization term within neural networks opens several avenues for future research. The approach offers a promising direction for enhancing feature alignment across domains in representation learning tasks. Additionally, this work calls attention to the potential benefits of deeper architectures and advanced pretraining techniques in further enhancing domain adaptation efficacy, particularly with more diverse and complex datasets.

Further research could investigate more advanced kernel functions for MMD computation, exploring their impact on adaptation performance across varied domain shifts. Moreover, extending the principles demonstrated in this work to other domains such as video, audio, and multimodal datasets could yield significant advances in the robustness and applicability of domain-adaptive learning systems.

In conclusion, the paper "Domain Adaptive Neural Networks for Object Recognition" enriches the discourse on domain adaptation by introducing a novel intersection of MMD measure and neural network training, setting a solid foundation for future empirical and theoretical advancements in this field.