Original Paper: https://arxiv.org/abs/2306.01195

By: Shuvendu Roy, Ali Etemad

Abstract:

We propose Consistency-guided Prompt learning (CoPrompt), a new fine-tuning method for vision-language models. Our approach improves the generalization of large foundation models when fine-tuned on downstream tasks in a few-shot setting. The basic idea of CoPrompt is to enforce a consistency constraint in the prediction of the trainable and pre-trained models to prevent overfitting on the downstream task. Additionally, we introduce the following two components into our consistency constraint to further boost the performance: enforcing consistency on two perturbed inputs and combining two dominant paradigms of tuning, prompting and adapter. Enforcing consistency on perturbed input serves to further regularize the consistency constraint, thereby improving generalization. Moreover, the integration of adapters and prompts not only enhances performance on downstream tasks but also offers increased tuning flexibility in both input and output spaces. This facilitates more effective adaptation to downstream tasks in a few-shot learning setting. Experiments show that CoPrompt outperforms existing methods on a range of evaluation suites, including base-to-novel generalization, domain generalization, and cross-dataset evaluation. On generalization, CoPrompt improves the state-of-the-art on zero-shot tasks and the overall harmonic mean over 11 datasets. Detailed ablation studies show the effectiveness of each of the components in CoPrompt. We make our code available at

this https URL

Summary Notes

CoPrompt: Transforming AI Fine-Tuning for Engineers

The landscape of Artificial Intelligence (AI) is ever-changing, with the fine-tuning of large, pre-trained models for specific tasks being a pivotal challenge.

CoPrompt emerges as an innovative solution, particularly for adapting vision-language models like CLIP. This blog post explores CoPrompt's approach, its benefits for AI engineers, and its impact on the field.

The Challenge

Adapting large-scale pre-trained models to specific tasks without losing their broad abilities has been a tough nut to crack.

Traditional methods like full fine-tuning and linear probing often compromise on performance, either diverging from the original model's capabilities or not adapting adequately to the new task.

Introducing CoPrompt

CoPrompt revolutionizes model adaptation by combining the strengths of prompting and adapters with consistency constraints.

This method minimizes deviation from the pre-trained model's behavior while allowing for task-specific tuning.

How CoPrompt Works

Basics

CoPrompt uses the transformer-based image and text encoders from CLIP as a foundation for its adaptation strategy.

Key Features

• Consistency Constraint: Ensures the model's embeddings remain close to their original state, using cosine similarity for measurement.
• Perturbed Inputs: Adds robustness by introducing variations in input data, improving the model's generalization from limited examples.
• Combining Prompting and Adapters: This blend allows for efficient and flexible model tuning.

Loss Function

CoPrompt employs a loss function that balances supervised learning with a consistency constraint, optimizing adaptation while retaining generalization.

CoPrompt's Performance

CoPrompt outperforms traditional methods in benchmarks, excelling in generalization from base to novel scenarios and across different datasets, thus proving its effectiveness and setting new standards.

The Significance of CoPrompt for AI Engineers

CoPrompt brings substantial benefits to AI engineers, particularly in the enterprise sector:

• Better Adaptability: It enables more precise tailoring of models to specific needs, opening up new possibilities and efficiencies.
• Generalization Preservation: By sticking close to the original model's capabilities, CoPrompt ensures that the broad applicability of pre-trained models isn't sacrificed for fine-tuning.
• Innovative Tuning Approaches: CoPrompt represents a shift towards a more integrated method of model adaptation, moving beyond the binary choice between full fine-tuning and linear probing.

Key Takeaways

CoPrompt redefines the approach to adapting vision-language models for specific tasks, striking a balance between maintaining the general capabilities of pre-trained models and achieving task-specific adaptability.

For AI engineers in the enterprise realm, it offers a potent tool to unlock the full potential of AI technologies.

Accessibility of CoPrompt

CoPrompt's methodology and implementation details are openly available on GitHub, encouraging further research and immediate practical application by AI engineers across various sectors.

Conclusion

CoPrompt stands as a significant advancement in AI fine-tuning, resolving the trade-off between model adaptability and generalization.

It paves new paths for AI applications and represents a cutting-edge solution for AI engineers looking to leverage AI's capabilities fully.