Deep Active Learning with Structured Neural Depth Search (2306.02808v1)

Abstract: Previous work optimizes traditional active learning (AL) processes with incremental neural network architecture search (Active-iNAS) based on data complexity change, which improves the accuracy and learning efficiency. However, Active-iNAS trains several models and selects the model with the best generalization performance for querying the subsequent samples after each active learning cycle. The independent training processes lead to an insufferable computational budget, which is significantly inefficient and limits search flexibility and final performance. To address this issue, we propose a novel active strategy with the method called structured variational inference (SVI) or structured neural depth search (SNDS) whereby we could use the gradient descent method in neural network depth search during AL processes. At the same time, we theoretically demonstrate that the current VI-based methods based on the mean-field assumption could lead to poor performance. We apply our strategy using three querying techniques and three datasets and show that our strategy outperforms current methods.

• The paper presents Active-SNDS, integrating structured variational inference to dynamically search and optimize neural network depth during active learning cycles.
• It mitigates computational costs by avoiding repetitive independent training and efficiently adjusts network complexity as more data is labeled.
• Empirical evaluations on MNIST, CIFAR-10, and CIFAR-100 show that the approach outperforms existing methods like Active-iNAS in performance and efficiency.

Deep Active Learning with Structured Neural Depth Search

The paper under discussion introduces an innovative framework in the domain of deep active learning combined with neural architecture search, titled "Deep Active Learning with Structured Neural Depth Search". This work addresses key constraints in existing methods by proposing a structured variational inference approach to dynamically search and optimize neural network depth during active learning cycles.

Overview of Challenges and Approach

Active learning traditionally aims to enhance a model’s performance by selecting the most informative samples to label. Previous architectures, such as Active-iNAS, have attempted to dynamically increase neural capacity in response to the expansion of labeled datasets. However, these methods are hampered by substantial computational costs due to repeated independent training processes, which substantially reduce search flexibility and overall efficiency.

To mitigate these computational inefficiencies, the authors propose the utilization of structured neural depth search (SNDS) facilitated by structured variational inference (SVI). This approach leverages variational inference to more effectively integrate network depth search into the active learning process through gradient descent.

Technical Contributions

1. Structured Variational Inference (SVI): The proposed method employs SVI as an alternative to the traditional mean-field assumption in variational inference. The mean-field assumption, which presumes independence between neural weights and architecture depth, is shown to potentially degrade performance by favoring shallow network architectures. In contrast, SVI establishes dependency between architectural depth and network weights, thereby improving the fidelity of posterior approximations over depth configurations.
2. Active-SNDS Algorithm: The paper introduces Active-SNDS, an active learning strategy that utilizes the SNDS method for efficient depth search. This strategy is implemented using pseudo-uniform sampling methods across active learning cycles to methodically adjust neural capacities.
3. Empirical Evaluation: The efficacy of the method is demonstrated through comprehensive experiments using three datasets—MNIST, CIFAR-10, and CIFAR-100. The results indicate that Active-SNDS not only outperforms existing methods including Active-iNAS and mean-field VI based approaches but also adjusts the neural network complexity aligns with task complexity over the active learning process.

Implications and Future Directions

The introduction of SVI in the context of neural architecture search during active learning cycles is a significant advancement for the automatic adaptation of model complexity according to data availability and task difficulty. The ability to efficiently explore depth configurations without extensive computational overhead opens avenues for the application of deep learning models in domains with restricted computational resources or labeled data—medical imaging being a prime example.

Moving forward, the implications of this approach suggest several avenues for further research:

• Investigation into the integration of other architectural components beyond depth, such as layer types and connections.
• Expansion into multi-task learning scenarios where shared architectures need to adapt to varying task complexities.
• Exploration of SVI applicability in other domains where traditional NAS techniques have shown limitations.

In conclusion, this paper contributes a robust methodological framework that combines the strengths of neural architecture search and active learning, showcasing the benefit of structured depth search in optimizing the efficiency and performance of deep learning models.