Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model

Abstract: Training deep networks requires various design decisions regarding for instance their architecture, data augmentation, or optimization. In this work, we find these training variations to result in networks learning unique feature sets from the data. Using public model libraries comprising thousands of models trained on canonical datasets like ImageNet, we observe that for arbitrary pairings of pretrained models, one model extracts significant data context unavailable in the other -- independent of overall performance. Given any arbitrary pairing of pretrained models and no external rankings (such as separate test sets, e.g. due to data privacy), we investigate if it is possible to transfer such "complementary" knowledge from one model to another without performance degradation -- a task made particularly difficult as additional knowledge can be contained in stronger, equiperformant or weaker models. Yet facilitating robust transfer in scenarios agnostic to pretrained model pairings would unlock auxiliary gains and knowledge fusion from any model repository without restrictions on model and problem specifics - including from weaker, lower-performance models. This work therefore provides an initial, in-depth exploration on the viability of such general-purpose knowledge transfer. Across large-scale experiments, we first reveal the shortcomings of standard knowledge distillation techniques, and then propose a much more general extension through data partitioning for successful transfer between nearly all pretrained models, which we show can also be done unsupervised. Finally, we assess both the scalability and impact of fundamental model properties on successful model-agnostic knowledge transfer.

• The paper demonstrates that complementary knowledge exists among pretrained models, enabling effective transfer even when performance metrics vary.
• It introduces a continual learning approach that uses data partitioning to overcome catastrophic forgetting during teacher-student integration.
• Experiments report a 92.5% success rate in knowledge improvement, suggesting new pathways for model enhancement using complementary insights.

Evaluating the Efficacy of General Knowledge Transfer between Pretrained Models

The paper "Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model" presents an in-depth analysis of knowledge transfer capabilities among pretrained models. This study seeks to understand whether pretrained models can exchange data context and semantic information regardless of their performance metrics or architectural differences. The researchers propose a continual learning approach to facilitate this knowledge transfer while retaining the pretrained model's initial learned context.

Complementary Knowledge Exploration

The inquiry starts with a review of pretrained models trained on canonical datasets like ImageNet. The authors hypothesize that these models, due to variations in training setups, learn unique features from the data, leading to "complementary knowledge." This knowledge, available in one pretrained model but not in another, forms the core investigation point for potential transfer benefits. Models are assessed based on their ability to correct misclassified samples by other models, termed positive prediction flips. The paper reports a significant presence of complementary knowledge among most model pairings, even when one model is considerably weaker by traditional performance metrics.

Challenges with Conventional Knowledge Distillation

Standard knowledge distillation paradigms, often relying on soft target matching between teacher and student models, are not straightforwardly applicable to already trained student models. Here, knowledge distillation aims to assimilate new information into a model without performance degradation or "catastrophic forgetting." Traditional frameworks such as KL divergence-based soft target alignment lack the capability to handle pretrained students efficiently, as demonstrated by high performance drops during such processes in exploratory evaluations.

Data Partitioning for Optimized Transfer

To overcome the limitations of regular knowledge distillation, the authors propose an innovative use of data partitioning under a continual learning framework. This involves separating training data into those samples best leveraged through the teacher model's knowledge and ones where the student's current knowledge should remain undisturbed. This partition is based on the model's confidence in prediction, enabling an unsupervised setup. The continual learning approach is shown to improve transfer rates significantly—92.5% success in knowledge improvement, a drastic improvement from less than 40% with conventional methods.

Practical Implications and Future Work

The findings suggest new pathways in leveraging vast model repositories for enhanced performance through complementary knowledge sharing. The proposed system could lead to reduced dependency on large datasets for model improvement and augment upscale models using lower-resource models effectively.

A primary consideration for future research lies in refining and understanding the model properties that correlate with greater receptivity to knowledge transfer. Furthermore, expanding domains, addressing non-vision tasks, and examining scalability in diverse machine learning applications remain essential steps to generalizing this study's results. Additionally, exploring multi-teacher knowledge transfer through strategically ordered sequential learning provides another angle pointing towards a richer training paradigm.

The paper sets foundational work for open-ended questions about the landscape of pretrained models and indicates promising directions for improving model utility by tapping into the latent collaboration among previously isolated learning systems.