Chapter 1: The Challenge of Knowledge Transfer in Cybernetics

Transferring knowledge from one model to another is more complex than it appears. Even if a model has acquired intricate insights from a dataset, this does not guarantee that it can relay that knowledge effectively to another model. This raises critical questions: How do we assess the expertise of a teacher model? What metrics can we use to evaluate the knowledge that is successfully imparted to the student? How can we enhance the efficiency of this transfer?

"I touch the future. I teach." — Christa McAuliffe

In contemporary practice, the norm is to utilize a dataset to train a model. This process involves numerous decisions based on the dataset, including architecture, data sampling, augmentation, and training protocols. Each of these choices influences the type of information the model can extract and essentially defines its unique capabilities.

This leads us to ponder several questions: How much semantic knowledge is shared among models trained on the same dataset, despite their differences? Is it possible to extract this knowledge? What are the limits of knowledge distillation? Can knowledge that the teacher model possesses but the student does not be transferred? How does the extraction of knowledge depend on hyperparameters? Finally, how can we facilitate knowledge transfer without compromising performance?

A recent article attempts to address these queries: Teaching What One Has Learned.

"A good education can change anyone. A good teacher can change everything!" — Unknown

In recent years, numerous models have been developed based on established datasets like ImageNet. Despite leveraging the same dataset, these models often exhibit variations in architecture and training methodologies, as seen in systematic collections like PyTorch Image Models (timm).

The authors investigated whether complementary knowledge exists between a teacher model and a student model. This complementary knowledge refers to the insights that have not been successfully conveyed from the teacher to the student. To quantify this knowledge, the authors devised an innovative method: they calculated the proportion of samples that the teacher correctly classified but the student did not.

The study involved 466 pairs of teacher-student models (301 unique architectures). The authors observed numerous instances where the teacher made accurate predictions while the student faltered. Even when the student outperformed the teacher (a weak teacher), discrepancies persisted. This indicates that knowledge transfer is not always complete and suggests the presence of untapped complementary knowledge.

The authors further examined the distribution of this complementary knowledge among various classes to determine whether it is clustered within specific subsets or evenly spread out. They found that in the case of weak teachers, certain classes exhibited disproportionate context, while strong teachers showed a more even distribution.

In summary, complementary knowledge exists between any pair of pre-trained models, with the potential for a teacher model to impart expertise to the student in areas that are semantically related. This discovery encourages further exploration into general-purpose knowledge transfer tools.

The first video discusses the shift from 'studying' to learning in the digital age, emphasizing the need to rethink educational practices.

How can we enhance the transfer of this complementary knowledge?

Knowledge Distillation (KD), as originally described by Hinton (2015), involves distilling knowledge from a pre-trained teacher model to an untrained student model. However, recent trends have shifted towards utilizing pre-trained models for specific tasks. For instance, a large model (e.g., over 100 billion parameters) can be distilled into a smaller model (e.g., under 10 billion parameters). This approach is more effective, as it is easier to train a student with some foundational knowledge for a specific topic.

In this context, conventional distillation methods often struggle to transfer knowledge between two already trained models without performance loss. Knowledge transfer can still be evaluated based on the changes in the student's top-1 accuracy.

A variant of KD appears in continual learning, where the student receives new signals from the teacher throughout the same transfer data. Here, regularization techniques are beneficial, albeit challenging to implement. Instead of imposing restrictions at the weight level, the authors propose data-level regularization by categorizing transfer data into samples where the student can gain from teacher feedback versus those requiring retention of prior knowledge.

The second video features Michelle Jasper from the Master of Applied Cybernetics 2022 cohort, showcasing innovative approaches in the field.

If a sample shows the teacher performing better, knowledge is distilled from the teacher. Conversely, if the student is correct, the focus is on maintaining the student's initial behavior. The authors also utilize probabilities assigned by both teacher and student models for an unsupervised process.

In essence, if the teacher model assigns a higher probability for the correct class, that output is adopted; otherwise, the student's output is used.

The authors also suggest multi-teacher extensions with three methods:

1. Parallel: All teacher models are utilized simultaneously for knowledge transfer.
2. Sequential: Each teacher's knowledge is transferred one after the other, treating the distilled student as a new student after each step.
3. Model Soup: Each student is distilled from a separate teacher model, followed by weight interpolation among all models.

What happens when transferring knowledge between two pre-trained models?

The authors analyzed 400 teacher-student pairs and found that traditional knowledge distillation is often ineffective in transferring knowledge. This is particularly true for weak teachers, where performance may degrade during the transfer process. However, continual learning methods have shown success in facilitating knowledge transfer.

Overall, the results demonstrate promising success rates with consistent improvements across various teacher-student pairs. While the gains might not always match the minimal amount of complementary knowledge identified, they serve as a strong proof-of-concept for future research and the potential for effective general knowledge transfer.

The authors also delve into the origins of student performance improvements, questioning whether they stem from the transfer of complementary knowledge. Notably, the best transfer occurs in the teacher model's areas of expertise, suggesting the importance of selecting a teacher aligned with the student's needs.

In conclusion, this research highlights that any pairing of models trained on the same dataset with different training protocols reveals substantial complementary knowledge. The findings underscore the necessity for effective transfer methods that enhance knowledge sharing without performance losses.

This exploration also reflects Google's significant investment in distillation, as they leverage vast foundational models to create smaller, task-specific models that are more efficient and accessible.

What are your thoughts? Would you consider experimenting with these methods? Share your insights in the comments!

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