CLIP-Embed-KD: Computationally Efficient Knowledge Distillation Using Embeddings as Teachers (2404.06170v1)

Abstract: Contrastive Language-Image Pre-training (CLIP) has been shown to improve zero-shot generalization capabilities of language and vision models. In this paper, we extend CLIP for efficient knowledge distillation, by utilizing embeddings as teachers. Typical knowledge distillation frameworks require running forward passes through a teacher model, which is often prohibitive in the case of billion or trillion parameter teachers. In these cases, using only the embeddings of the teacher models to guide the distillation can yield significant computational savings. Our preliminary findings show that CLIP-based knowledge distillation with embeddings can outperform full scale knowledge distillation using $9\times$ less memory and $8\times$ less training time. Code available at: https://github.com/lnairGT/CLIP-Distillation/

• The paper introduces CLIP-Embed-KD, which leverages precomputed teacher embeddings to efficiently align student models.
• Performance tests show up to 9x lower memory use and 8x faster training while maintaining competitive accuracy.
• The approach offers a scalable solution for resource-efficient knowledge transfer in vision tasks using contrastive language-image cues.

Extending CLIP for Efficient Knowledge Distillation

Introduction to CLIP and Knowledge Distillation

Contrastive Language-Image Pre-training (CLIP) has emerged as a powerful method for learning visual concepts through natural language supervision, exhibiting robust zero-shot transfer capabilities across a variety of classification tasks. This approach aligns image and text modalities via a contrastive objective function, leveraging the scaled pairwise cosine similarity between image and text embeddings. Knowledge distillation (KD), on the other hand, transfers knowledge from a large teacher model to a smaller student model, aiming to replicate the teacher's performance with less computational overhead. Conventional KD techniques, however, necessitate extensive computational resources, particularly when dealing with billion-parameter teacher models.

Proposed Methodologies

In exploring the integration of CLIP within the KD framework, this paper presents two novel methodologies:

1. CLIP-Teacher-KD: This approach directly applies CLIP's contrastive pre-training objective to the KD process, requiring the computation of teacher and student embeddings through successive forward passes.
2. CLIP-Embed-KD: An extension of the above, this method utilizes pre-computed teacher embeddings to align the student model, significantly reducing the need for repeated forward passes through the teacher model.

The primary objective of this exploration is to ascertain whether CLIP can facilitate more computationally efficient KD by leveraging teacher model embeddings.

Approach and Implementation

The approach encompasses the decomposition of KD into its fundamental components—teacher and student models, alongside a distillation loss function designed to minimize their output disparities. For CLIP-Teacher-KD, embedding vectors from both teacher and student models are extracted, normalized, and compared using a dot product to calculate a CLIP-based distillation loss. CLIP-Embed-KD refines this by employing a set of averaged class embeddings derived from the teacher model, thereby obviating the need for its activation during student training.

Experimental Insights

Experimentation focused on CIFAR100 image classification tasks using Vision Transformers (ViT) as both student and teacher models. The paper compared the performance of CLIP-Teacher-KD and CLIP-Embed-KD against traditional KD metrics, revealing several key insights:

• Performance Metrics: CLIP-embed-KD demonstrated a close performance to CLIP-Teacher-KD while offering significant reductions in memory usage and computational time, showcasing 9 times less memory and 8 times less training time.
• Computational Efficiency: Particularly for CLIP-Embed-KD, using averaged teacher embeddings instead of full model forwarding provided substantial computational savings without considerably compromising the accuracy.
• Scalability: The findings suggested potential scalability of CLIP-Embed-KD to larger models and image sizes, hinting at the versatility of this method across different datasets and model sizes.

Conclusions and Future Directions

The paper elucidates the viability of embedding-based knowledge distillation as a method to mitigate the computational demands of conventional KD techniques. While the research does not claim state-of-the-art performances, it does highlight the potential of leveraging CLIP for more resource-efficient knowledge transfer. Future endeavors will explore alternative representations for teacher embeddings to further enhance student model performance, alongside assessments across diverse datasets and potentially extending the methodology to NLP models.

In essence, this exploration into CLIP-based KD proposes a promising direction for efficient model training, particularly in scenarios where computational resources are limited. The implications of this research are significant, offering a pathway to democratizing access to high-performing AI models across various fields and applications.