Learning Deep Representations with Probabilistic Knowledge Transfer (1803.10837v3)

Abstract: Knowledge Transfer (KT) techniques tackle the problem of transferring the knowledge from a large and complex neural network into a smaller and faster one. However, existing KT methods are tailored towards classification tasks and they cannot be used efficiently for other representation learning tasks. In this paper a novel knowledge transfer technique, that is capable of training a student model that maintains the same amount of mutual information between the learned representation and a set of (possible unknown) labels as the teacher model, is proposed. Apart from outperforming existing KT techniques, the proposed method allows for overcoming several limitations of existing methods providing new insight into KT as well as novel KT applications, ranging from knowledge transfer from handcrafted feature extractors to {cross-modal} KT from the textual modality into the representation extracted from the visual modality of the data.

• The paper introduces a novel probabilistic method that minimizes KL divergence between feature distributions to enhance deep representation learning.
• It employs cosine similarity-based affinity metrics and kernel density estimation to flexibly transfer knowledge across models with varying architectures.
• Experiments on CIFAR-10, YouTube Faces, SUN Attributes, and PASCAL VOC demonstrate significant improvements in classification and detection performance.

Learning Deep Representations with Probabilistic Knowledge Transfer

The paper "Learning Deep Representations with Probabilistic Knowledge Transfer" by Nikolaos Passalis and Anastasios Tefas presents a novel approach to knowledge transfer (KT) by leveraging probabilistic methods to facilitate the transfer of knowledge between neural networks. Traditionally, KT techniques have focused on transferring knowledge specifically for classification tasks, which limits their applicability to broader representation learning applications. This paper introduces a new probabilistic approach that focuses on matching the probability distribution of feature spaces rather than the specific output representations of teacher and student models.

Overview and Methodology

The authors introduce a probabilistic KT method that models the interactions between data samples in the feature space through probability distributions. Unlike traditional KT methods, which often regress the output of a teacher model directly, this technique employs kernel density estimation to establish joint probability distributions and minimizes the Kullback-Leibler divergence between these distributions for the teacher and student models. This probabilistic framework provides advantages over classical methods, allowing for effective knowledge transfer even when the output dimensionalities of the networks differ.

Key features of this method include:

• The use of cosine similarity-based affinity metrics to estimate probability distributions. This aids in relaxing constraints typically required by Gaussian kernel approaches, ensuring robust affinity estimations.
• A divergence metric (KL divergence) is used to facilitate the training of the student model, providing a mechanism that accentuates local spatial fidelity rather than entire geometric congruence.
• A flexible framework that can incorporate domain knowledge or external information sources to enhance probability estimations, which is pivotal for expanding KT applications.

Experimental Evaluation

The empirical validation highlights several scenarios where the novel KT approach excels. The experiments involve transferring knowledge from a deep neural network on the CIFAR-10 dataset, from handcrafted features on the YouTube Faces dataset, cross-modally on the SUN Attribute dataset, and between object detection networks on the PASCAL VOC dataset.

1. Deep Neural Networks: The paper demonstrates superior performance of the probabilistic KT method compared to baseline and hint-based methods on CIFAR-10, showcasing significant improvements in mean Average Precision (mAP) and top-k precision metrics.
2. Handcrafted Feature Extractors: The proposed KT method was evaluated on the YouTube Faces dataset, outperforming conventional techniques. Impressively, it allows the integration of domain knowledge to further improve representation quality, showing adaptability and utility beyond traditional KT practices.
3. Cross-modal Transfer: Exploiting the multi-modal characteristics of the SUN Attribute dataset, the method effectively transfers knowledge from textual to visual modalities, indicating its applicability in cross-modal scenarios.
4. Object Detection: For the PASCAL VOC dataset, the KT framework aids in boosting performance over conventional training, particularly for object detection tasks where pre-trained model availability is limited.

Implications and Future Directions

This probabilistic approach opens several avenues in both practical implementations and theoretical explorations of KT in neural networks. By decoupling KT from strict architectural constraints and extending it to different target tasks beyond classification, it offers a versatile toolset for researchers. Moreover, the connection to mutual information theory provides theoretical backing that can be further explored for other data-driven applications.

Future research could investigate optimizing kernel choice and divergence metrics for specific tasks, exploring alternative probabilistic frameworks, and extending this method to leverage ensemble models or hybrid feature spaces. Additionally, integration with unsupervised or semi-supervised learning paradigms could broaden its applicability even further.

In summary, this paper contributes significantly to the field of knowledge transfer by proposing a probabilistic method that generalizes beyond the limitations of task-specific KT methods, paving the way for more efficient use of lightweight neural networks across a wide range of applications.