TiC-CLIP: Continual Training of CLIP Models (2310.16226v3)

Abstract: Keeping large foundation models up to date on latest data is inherently expensive. To avoid the prohibitive costs of constantly retraining, it is imperative to continually train these models. This problem is exacerbated by the lack of any large scale continual learning benchmarks or baselines. We introduce the first set of web-scale Time-Continual (TiC) benchmarks for training vision-LLMs: TiC-DataComp, TiC-YFCC, and TiC-Redcaps. TiC-DataComp, our largest dataset, contains over 12.7B timestamped image-text pairs spanning 9 years (2014-2022). We first use our benchmarks to curate various dynamic evaluations to measure temporal robustness of existing models. We show OpenAI's CLIP (trained on data up to 2020) loses $\approx 8\%$ zero-shot accuracy on our curated retrieval task from 2021-2022 compared with more recently trained models in OpenCLIP repository. We then study how to efficiently train models on time-continuous data. We demonstrate that a simple rehearsal-based approach that continues training from the last checkpoint and replays old data reduces compute by $2.5\times$ when compared to the standard practice of retraining from scratch. Code is available at https://github.com/apple/ml-tic-clip.

• The paper presents temporal benchmarks for CLIP models that reveal an 8% drop in zero-shot accuracy on older datasets.
• It proposes a rehearsal-based training method that reduces computational overhead by 2.5 times compared to retraining from scratch.
• The research highlights the importance of continual updates for foundation models, offering scalable and adaptive strategies for multimodal systems.

An Analytical Exploration of "TiC-CLIP: Continual Training of CLIP Models"

The paper "TiC-CLIP: Continual Training of CLIP Models" introduces a novel approach to address the computational challenges of keeping large multimodal foundation models, specifically CLIP models, current with evolving data. The authors propose Time-Continual (TiC) training benchmarks designed to facilitate efficient continual learning of vision-LLMs. These benchmarks comprise over 12.7 billion timestamped image-text pairs collected over eight years (2014-2022), providing a robust foundation for evaluating temporal robustness.

Key Contributions

1. Temporal Benchmarks for Vision-LLMs: The paper presents a comprehensive set of web-scale benchmarks tailored for continual training, focusing on time-evolving data. This innovation addresses the absence of large-scale continual learning benchmarks for vision-LLMs.
2. Assessment of Temporal Robustness: The authors employ their benchmarks to conduct dynamic evaluations of CLIP models, revealing that models trained on older datasets exhibit noticeable performance degradation—specifically, an 8% drop in zero-shot accuracy compared to models trained on more recent data.
3. Efficient Continual Training Methodology: A rehearsal-based approach is proposed, which incorporates training from the last checkpoint while replaying old data. This method reduces computational overhead significantly (by 2.5 times) compared to traditional retraining from scratch, thus providing a cost-efficient solution for maintaining model currency with minimal loss in performance.

Numerical Results and Claims

The paper asserts significant findings with strong numerical backing. For instance, by deploying their continual training strategy, the researchers were able to maintain competitive performance levels on dynamic tasks while ensuring computational efficiency. This is particularly emphasized through the reduction in computational costs without marked degradation in retrieval task performance—a noteworthy achievement given the expansive temporal scope of the datasets used.

Theoretical and Practical Implications

Theoretically, this research underscores the limitations of static benchmarks like ImageNet for evaluating the temporal adaptability of foundation models. It challenges the conventional approach of periodically retraining models from scratch, which is resource-intensive. Practically, the insights from this work could guide the development of scalable and adaptive models, which are crucial for applications in rapidly changing environments such as social media analytics and real-time image retrieval systems.

Future Directions

The findings from this paper pave the way for future research in several domains:

• Exploration of Buffer Management Strategies: Further studies could aim to optimize data storage techniques for replaying old data more effectively, considering the computational constraints of large-scale deployments.
• Granular Data Streaming Research: Extending the research paradigm to explore data streaming at finer granularity, such as monthly data updates, could enhance the model's ability to adapt more incrementally and seamlessly.
• Advanced Learning Rate Schedules: Investigating alternative learning rate schedules that are tailored to continual learning scenarios may yield further improvements in training efficiency and stability.
• Expansion to Other Foundation Models: While focusing on CLIP models, the principles established in this work could potentially apply to the broader class of foundation models, including those used in language processing and other multimodal applications.

This paper's contribution lies not just in the proposed methodologies and benchmarks, but also in the critical reflection it encourages on the current practices of training large-scale machine learning models, offering a path towards more sustainable and adaptive AI systems.