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Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies (2404.08197v2)

Published 12 Apr 2024 in cs.CV
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies

Abstract: This paper investigates the performance of the Contrastive Language-Image Pre-training (CLIP) when scaled down to limited computation budgets. We explore CLIP along three dimensions: data, architecture, and training strategies. With regards to data, we demonstrate the significance of high-quality training data and show that a smaller dataset of high-quality data can outperform a larger dataset with lower quality. We also examine how model performance varies with different dataset sizes, suggesting that smaller ViT models are better suited for smaller datasets, while larger models perform better on larger datasets with fixed compute. Additionally, we provide guidance on when to choose a CNN-based architecture or a ViT-based architecture for CLIP training. We compare four CLIP training strategies - SLIP, FLIP, CLIP, and CLIP+Data Augmentation - and show that the choice of training strategy depends on the available compute resource. Our analysis reveals that CLIP+Data Augmentation can achieve comparable performance to CLIP using only half of the training data. This work provides practical insights into how to effectively train and deploy CLIP models, making them more accessible and affordable for practical use in various applications.

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Scaling Down CLIP: An In-depth Analysis of Data, Architecture, and Training Strategies

Introduction

Contrastive Language-Image Pre-training (CLIP) has emerged as a state-of-the-art framework in the domain of image and language representation learning, demonstrating exceptional performance across a variety of downstream tasks. However, the exploration of CLIP's efficacy under constrained computational resources remains largely unexplored. This paper presents a comprehensive paper focusing on the scaling down of CLIP across three critical dimensions: data, architecture, and training strategies. This research endeavors to provide actionable insights into optimizing CLIP's training and deployment in scenarios bounded by computational budgets, thereby broadening its applicability.

Scaling Data: Quantity, Quality, and Impact

The paper meticulously explores the interplay between data quantity and model performance, making several key observations:

  • Data Quantity: The investigations reveal that the model's performance on ImageNet and its variants is significantly influenced by dataset size and the number of training epochs. Notably, while larger datasets benefit from increased training epochs, smaller datasets do not exhibit marked improvements beyond a certain point.
  • Data Quality: A surprising insight from the paper highlights the importance of data quality. It was found that models trained on a smaller subset comprising the top 40% of high-quality data outperformed those trained on the entire dataset. This underscores the critical impact of data quality on model performance, suggesting that data curation strategies focusing on quality could lead to more effective training than merely increasing data quantity.

Exploration of Architectures

This section of the paper explores the comparison between CNN and ViT (Vision Transformer) architectures for CLIP training, elucidating how different architectures fare under various dataset sizes:

  • The findings suggest that CNN architectures may exhibit superior performance on smaller datasets due to their inherent local structure bias, whereas ViT architectures outshine on larger data scales.
  • An intriguing observation is that larger ViT models do not always guarantee better performance, especially when the data scale is limited, hinting at a nuanced relationship between architecture effectiveness and dataset size.

Training Strategies and Their Trade-offs

The comparison among several CLIP training strategies reveals nuanced trade-offs between computational cost and model performance:

  • The paper comparatively assesses SLIP, FLIP, CLIP, and CLIP+Data Augmentation strategies, finding that the efficacy of each strategy varies with the available computational budget.
  • Particularly, CLIP+Data Augmentation emerges as a promising strategy, achieving comparable performance to the vanilla CLIP model with significantly reduced data requirement, thereby offering a cost-efficient alternative for training CLIP models.

Implications and Future Directions

  • Practical Deployment: The insights from this paper have profound implications for deploying CLIP in resource-constrained environments, suggesting pathways to maintain or even enhance performance without proportional increases in computational demand.
  • Theory of Scale in AI Models: The observations contribute to the broader discourse on the scalability of AI models, challenging the prevailing notion that larger models and datasets invariably lead to better performance. Instead, they advocate for a nuanced approach that balances data quality, model architecture, and training strategies.
  • Future Research: There's an avenue for further exploration into dynamic training strategies that adaptively adjust the architecture and training regimen based on the evolving quality and quantity of training data. Additionally, investigating the transferability of these insights to other domains of AI could unveil universal principles of scalable model training.

In conclusion, this paper not only provides comprehensive analysis and practical insights into scaling down CLIP models but also opens up new vistas for research into efficient model training paradigms. The nuanced understanding of how data management, architectural choices, and training strategies interact to affect model performance could significantly influence future developments in the field of artificial intelligence.

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References (35)
  1. Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models. In Neural Information Processing Systems, 2019.
  2. Pali: A jointly-scaled multilingual language-image model. ArXiv, abs/2209.06794, 2022.
  3. Microsoft coco captions: Data collection and evaluation server. ArXiv, abs/1504.00325, 2015.
  4. Uniter: Universal image-text representation learning. In European Conference on Computer Vision, 2019.
  5. Reproducible scaling laws for contrastive language-image learning. ArXiv, abs/2212.07143, 2022.
  6. Randaugment: Practical automated data augmentation with a reduced search space. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.  3008–3017, 2019.
  7. Imagenet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.  248–255, 2009.
  8. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp.  4171–4186, 2019.
  9. An image is worth 16x16 words: Transformers for image recognition at scale. ArXiv, abs/2010.11929, 2020.
  10. No one representation to rule them all: Overlapping features of training methods. arXiv preprint arXiv:2110.12899, 2021.
  11. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.  770–778, 2016.
  12. Natural adversarial examples. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.  15257–15266, 2019.
  13. The many faces of robustness: A critical analysis of out-of-distribution generalization. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp.  8320–8329, 2020.
  14. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.  4700–4708, 2017.
  15. Openclip, July 2021. URL https://doi.org/10.5281/zenodo.5143773. If you use this software, please cite it as below.
  16. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, 2021.
  17. Supervised contrastive learning. ArXiv, abs/2004.11362, 2020.
  18. Scaling language-image pre-training via masking. ArXiv, abs/2212.00794, 2022.
  19. Swin transformer: Hierarchical vision transformer using shifted windows. arXiv preprint arXiv:2103.14030, 2021.
  20. A convnet for the 2020s. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.  11966–11976, 2022.
  21. Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization. ArXiv, abs/2107.04649, 2021.
  22. Slip: Self-supervision meets language-image pre-training. ArXiv, abs/2112.12750, 2021.
  23. Quality not quantity: On the interaction between dataset design and robustness of clip. ArXiv, abs/2208.05516, 2022.
  24. Deep contextualized word representations. arXiv preprint arXiv:1802.05365, 2018.
  25. Combined scaling for zero-shot transfer learning. ArXiv, abs/2111.10050, 2021.
  26. Improving language understanding by generative pre-training. OpenAI Blog, 2018.
  27. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, 2021.
  28. Hierarchical text-conditional image generation with clip latents. ArXiv, abs/2204.06125, 2022.
  29. Do imagenet classifiers generalize to imagenet? In International Conference on Machine Learning, pp.  5389–5400, 2019.
  30. Adafactor: Adaptive learning rates with sublinear memory cost. ArXiv, abs/1804.04235, 2018.
  31. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
  32. Measuring robustness to natural distribution shifts in image classification. ArXiv, abs/2007.00644, 2020.
  33. Mlp-mixer: An all-mlp architecture for vision. In Neural Information Processing Systems, 2021.
  34. Learning robust global representations by penalizing local predictive power. In Neural Information Processing Systems, 2019.
  35. Lit: Zero-shot transfer with locked-image text tuning. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.  18102–18112, 2021.
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Authors (3)
  1. Zichao Li (36 papers)
  2. Cihang Xie (91 papers)
  3. Ekin Dogus Cubuk (20 papers)
Citations (4)
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  1. "Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies", Li et al 2024 (10 points, 1 comment)