UNIC: Universal Classification Models via Multi-teacher Distillation (2408.05088v1)

Abstract: Pretrained models have become a commodity and offer strong results on a broad range of tasks. In this work, we focus on classification and seek to learn a unique encoder able to take from several complementary pretrained models. We aim at even stronger generalization across a variety of classification tasks. We propose to learn such an encoder via multi-teacher distillation. We first thoroughly analyse standard distillation when driven by multiple strong teachers with complementary strengths. Guided by this analysis, we gradually propose improvements to the basic distillation setup. Among those, we enrich the architecture of the encoder with a ladder of expendable projectors, which increases the impact of intermediate features during distillation, and we introduce teacher dropping, a regularization mechanism that better balances the teachers' influence. Our final distillation strategy leads to student models of the same capacity as any of the teachers, while retaining or improving upon the performance of the best teacher for each task. Project page and code: https://europe.naverlabs.com/unic

• The paper introduces a framework that distills knowledge from multiple pretrained teacher models to train a robust universal classification model.
• It employs techniques such as feature standardization, dedicated projectors, and a ladder of projectors to enhance accuracy on tasks like ImageNet classification and segmentation.
• Teacher dropping regularization ensures balanced learning from all teachers and improves performance under domain shifts and diverse applications.

Evaluating Multi-Teacher Distillation for Universal Classification Models

Introduction

Recent advancements in deep learning have underscored the viability of pretrained models in achieving superior performance across diverse classification tasks. However, there is still a significant interest in developing models that not only leverage pretrained checkpoints but also maximize their performance through synergistic learning techniques. This paper presents a comprehensive investigation into multi-teacher distillation methodologies aimed at producing a single robust classification model. The authors propose a unique distillation framework and present several nuanced improvements designed to ensure the student model outperforms or matches the best teacher across various tasks.

Methodology

The core of this paper revolves around the notion of leveraging multiple strong teacher models using a distillation strategy that incorporates various novel techniques. Their primary methodology includes:

1. Multi-Teacher Distillation: Combining outputs from several pretrained encoders, each optimized for different aspects of visual recognition, to train a single student model.
2. Feature Standardization: Ensuring consistency in feature statistics across different teachers by normalizing outputs to have zero mean and unit variance.
3. Dedicated Projectors for CLS/Patch Tokens: Separate projectors for CLS and patch tokens to ensure appropriate learning given their distinct semantic roles, enhancing student model performance.
4. Ladder of Projectors: A construct where intermediate features from multiple layers contribute to the final distillation loss, ensuring richer feature representations during training.
5. Teacher Dropping Regularization: A dropout-like regularization technique that ensures balanced learning from all teachers by probabilistically ignoring certain teachers based on their loss magnitudes.

Results

The evaluation comprehensively covers several axes of classification performance:

1. ImageNet and Transfer Learning: The proposed encoder showcases significant improvements in classification tasks on datasets like ImageNet-1K and 15 other transfer learning datasets. Transfer learning performance particularly benefits from learning from diverse teachers.
2. Dense Prediction Tasks: Notably, the student model demonstrates a remarkable capacity for semantic segmentation and depth estimation, showcasing the effectiveness of patch feature representations.
3. Domain Shift Performance: The student models retain strong performance under domain shifts, maintaining high accuracy across datasets like ImageNet-R and ImageNet-Sketch.

Analysis and Observations

The extensive experiments illustrate several essential insights:

1. Feature Utilization: Models distilled using the proposed framework show lower redundancy in their weights and features, making them more effective in space utilization and resilient to dimensionality reduction.
2. Balanced Learning: Teacher dropping regularization significantly aids in balancing contributions from different teachers, crucially enhancing performance on tasks where certain teachers would otherwise dominate.
3. Scalability: The framework is flexible enough to benefit from adding more teachers. Even when weaker teachers are added, the overall performance of the student model further improves.

Practical and Theoretical Implications

This paper's implications span both practical applications and theoretical advancements:

1. Generalization Across Tasks: The student model's strong performance across varied datasets makes it a versatile tool for industry applications where diverse task requirements are common.
2. Knowledge Transfer: The framework provides a substantial advance in understanding how to synergize different pretrained models, contributing significantly to the theoretical foundations of knowledge distillation.
3. Efficiency: By effectively utilizing features and reducing redundancy, the framework points towards more computationally efficient models without compromising performance.

Future Directions

The research opens several promising avenues for future work:

1. Exploring Different Architectures: Investigating the efficacy of the proposed methods on other architectures besides ViTs, such as convolutional neural networks or hybrid models.
2. Enhanced Regularization Techniques: Further refining teacher dropping algorithms to optimize for a wider range of tasks and improve regularization efficacy.
3. Synthetic Data: Extending distillation techniques using synthetically generated datasets to potentially reduce dependency on large-scale annotated datasets.

Conclusion

The introduction of multi-teacher distillation with transformative techniques like feature standardization, dedicated projectors, a ladder of projectors, and teacher dropping regularization marks a significant stride towards universal classification models. The UNIC framework, as validated by extensive empirical analysis, represents a robust and versatile approach, advancing the state-of-the-art in general representation learning. This body of work underlines the importance of balanced, multi-faceted learning and sets the stage for further innovations in AI and machine learning domains.