Active Data Curation Effectively Distills Large-Scale Multimodal Models (2411.18674v1)

Abstract: Knowledge distillation (KD) is the de facto standard for compressing large-scale models into smaller ones. Prior works have explored ever more complex KD strategies involving different objective functions, teacher-ensembles, and weight inheritance. In this work we explore an alternative, yet simple approach -- active data curation as effective distillation for contrastive multimodal pretraining. Our simple online batch selection method, ACID, outperforms strong KD baselines across various model-, data- and compute-configurations. Further, we find such an active data curation strategy to in fact be complementary to standard KD, and can be effectively combined to train highly performant inference-efficient models. Our simple and scalable pretraining framework, ACED, achieves state-of-the-art results across 27 zero-shot classification and retrieval tasks with upto 11% less inference FLOPs. We further demonstrate that our ACED models yield strong vision-encoders for training generative multimodal models in the LiT-Decoder setting, outperforming larger vision encoders for image-captioning and visual question-answering tasks.

An Analysis of "Active Data Curation Effectively Distills Large-Scale Multimodal Models"

The paper "Active Data Curation Effectively Distills Large-Scale Multimodal Models" offers a detailed investigation into an innovative approach to model compression, focusing on knowledge distillation (KD) for multimodal models. The authors propose a novel method termed ACID (Active Curation as Implicit Distillation), which leverages active data curation as a mechanism for efficient knowledge transfer from large-scale teacher models to more compact student models.

Core Contributions and Methodology

The central innovation presented in the paper is the use of active data curation as a form of implicit distillation, setting the stage for an effective model compression framework without necessitating complex distillation objectives or techniques. This approach diverges from traditional KD strategies, which often involve intricate combinations of multiple objectives, predetermined architectures, and extensive parameter tuning.

ACID Mechanism: The method capitalizes on selective sampling of the training data to enhance the learning dynamics of smaller models. By curating data batches that maximize the difference between the student model’s current state and the teacher’s performance, ACID fosters an implicit transfer of knowledge. This is particularly advantageous as it simplifies the training pipeline, reducing the reliance on multiple teacher models or sophisticated weight sharing strategies typically seen in KD methodologies.

ACED Framework: Complementing the ACID strategy, the authors propose the ACED method, which combines the benefits of ACID with explicit distillation objectives. This combination aims to harness the full potential of both data-driven implicit distillation and conventional KD, bridging any performance gaps that occur with ACID alone.

Empirical Results and Implications

The empirical evaluations show that models trained with ACID outperform strong KD baselines across various configurations and tasks, including notable improvements on zero-shot classification and image-text retrieval tasks. The ACID approach is benchmarked against state-of-the-art methods, such as TinyCLIP and MobileCLIP, demonstrating superior performance with reduced computational overhead.

Numerical Evidence: The paper highlights significant performance gains in terms of ImageNet zero-shot transfer accuracy, which reached 73.7% with up to 11% less inference FLOPs compared to larger models. These results validate the efficiency and scalability of the ACID approach in training highly performant, inference-efficient models.

Theoretical and Practical Implications

From a theoretical perspective, ACID redefines the landscape for model distillation by treating active data selection as a form of KD. This insight indicates pathways for further research, potentially extending beyond multimodal contexts to other domains requiring efficient model compression without extensive architecture manipulation.

Practically, the simplification introduced by ACID can significantly reduce the complexity of deploying multimodal models on edge devices, where computational resources are limited. The synergy between active data curation and traditional KD, as demonstrated by ACED, suggests a holistic approach to model training that balances training efficiency with deployment constraints.

Future Directions

The framework set forth by the authors opens several avenues for future exploration. Extending the principles of ACID to different model architectures and training tasks beyond the scope of the current multimodal focus could provide deeper insights into its generalizability. Additionally, exploring the implications of data-curated learning in online training scenarios presents an intriguing area for future work.

In conclusion, "Active Data Curation Effectively Distills Large-Scale Multimodal Models" makes a substantive contribution to the field of efficient AI model deployment. By leveraging active data curation, it presents a scalable and efficacious pathway for knowledge distillation, which could potentially reshape practices in multimodal model training and beyond.