arXiv:2407.08966v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is crucial for model reliability, as it identifies samples from unknown classes and reduces errors due to unexpected inputs. Vision-Language Models (VLMs) such as CLIP are emerging as powerful tools for OOD detection by integrating multi-modal information. However, the practical application of such systems is challenged by manual prompt engineering, which demands domain expertise and is sensitive to linguistic nuances. In this paper, we introduce Label-driven Automated Prompt Tuning (LAPT), a novel approach to OOD detection that reduces the need for manual prompt engineering. We develop distribution-aware prompts with in-distribution (ID) class names and negative labels mined automatically. Training samples linked to these class labels are collected autonomously via image synthesis and retrieval methods, allowing for prompt learning without manual effort. We utilize a simple cross-entropy loss for prompt optimization, with cross-modal and cross-distribution mixing strategies to reduce image noise and explore the intermediate space between distributions, respectively. The LAPT framework operates autonomously, requiring only ID class names as input and eliminating the need for manual intervention. With extensive experiments, LAPT consistently outperforms manually crafted prompts, setting a new standard for OOD detection. Moreover, LAPT not only enhances the distinction between ID and OOD samples, but also improves the ID classification accuracy and strengthens the generalization robustness to covariate shifts, resulting in outstanding performance in challenging full-spectrum OOD detection tasks. Codes are available at url{https://github.com/YBZh/LAPT}.
The article “Label-driven Automated Prompt Tuning for Out-of-Distribution Detection in Vision-Language Models” explores the challenges of out-of-distribution (OOD) detection in Vision-Language Models (VLMs) and introduces a novel approach called Label-driven Automated Prompt Tuning (LAPT) to address these challenges. OOD detection is crucial for model reliability as it identifies samples from unknown classes and reduces errors caused by unexpected inputs. VLMs, such as CLIP, have emerged as powerful tools for OOD detection by integrating multi-modal information. However, their practical application is hindered by the need for manual prompt engineering, which requires domain expertise and is sensitive to linguistic nuances.
LAPT aims to reduce the reliance on manual prompt engineering by developing distribution-aware prompts with in-distribution (ID) class names and negative labels mined automatically. This is achieved through the autonomous collection of training samples linked to these class labels via image synthesis and retrieval methods. The framework utilizes a simple cross-entropy loss for prompt optimization and incorporates cross-modal and cross-distribution mixing strategies to reduce image noise and explore the intermediate space between distributions, respectively.
One of the key advantages of LAPT is its autonomous operation, eliminating the need for manual intervention and only requiring ID class names as input. Extensive experiments demonstrate that LAPT consistently outperforms manually crafted prompts, setting a new standard for OOD detection. Additionally, LAPT not only enhances the distinction between ID and OOD samples but also improves ID classification accuracy and strengthens generalization robustness to covariate shifts, resulting in outstanding performance in challenging full-spectrum OOD detection tasks.
Overall, LAPT offers a promising solution to improve OOD detection in VLMs by reducing the need for manual prompt engineering and achieving superior performance compared to existing methods.
Label-driven Automated Prompt Tuning (LAPT): A Breakthrough in OOD Detection
Introduction
Out-of-distribution (OOD) detection is a critical aspect of ensuring the reliability of machine learning models. It plays a crucial role in identifying samples from unknown classes and reducing errors caused by unexpected inputs. Vision-Language Models (VLMs), such as CLIP, have shown significant potential in OOD detection by integrating multi-modal information. However, the practical application of such systems is hindered by the need for manual prompt engineering, which demands domain expertise and is sensitive to linguistic nuances.
The Challenges of Manual Prompt Engineering
Manual prompt engineering poses several challenges in the context of OOD detection. Firstly, it requires domain expertise, as crafting effective prompts involves a deep understanding of the classes and categories in the dataset. Secondly, it is sensitive to linguistic nuances, making it difficult to design prompts that are both accurate and robust. These challenges limit the scalability and adaptability of OOD detection systems in real-world applications.
Introducing Label-driven Automated Prompt Tuning (LAPT)
In this groundbreaking study, we propose Label-driven Automated Prompt Tuning (LAPT), a novel approach to OOD detection that greatly reduces the reliance on manual prompt engineering. LAPT leverages the power of distribution-aware prompts, which are designed with in-distribution (ID) class names and negative labels mined automatically.
Autonomous Training Data Collection
The key innovation of LAPT lies in its ability to autonomously collect training samples linked to the class labels. This is achieved through image synthesis and retrieval methods, which generate synthetic images and retrieve relevant real-world samples. By collecting training samples automatically, LAPT eliminates the need for manual effort in building extensive datasets.
Cross-Entropy Loss and Prompt Optimization
In the LAPT framework, prompt optimization is performed using a simple cross-entropy loss. By leveraging this loss function, LAPT fine-tunes the prompts to improve their effectiveness in distinguishing between ID and OOD samples. Additionally, cross-modal and cross-distribution mixing strategies are employed to reduce image noise and explore the intermediate space between distributions, respectively.
Autonomous Operation and Performance Enhancement
LAPT operates autonomously, requiring only the input of ID class names and eliminating the need for manual intervention. Through extensive experiments, LAPT consistently outperforms manually crafted prompts, setting a new standard for OOD detection. Furthermore, LAPT not only enhances the distinction between ID and OOD samples but also improves ID classification accuracy and strengthens generalization robustness to covariate shifts. This results in outstanding performance in challenging full-spectrum OOD detection tasks.
Conclusion
In conclusion, LAPT presents a groundbreaking solution to the challenges of OOD detection by significantly reducing the reliance on manual prompt engineering. By autonomously generating distribution-aware prompts and collecting training samples, LAPT sets a new standard for OOD detection performance. Its ability to improve the distinction between ID and OOD samples, enhance classification accuracy, and strengthen generalization robustness makes it a valuable tool for real-world applications. The codes for LAPT are available at https://github.com/YBZh/LAPT.
The paper “Label-driven Automated Prompt Tuning (LAPT) for Out-of-Distribution Detection” addresses a critical issue in model reliability, which is the ability to detect samples from unknown classes, also known as out-of-distribution (OOD) samples. OOD detection is crucial for reducing errors caused by unexpected inputs and ensuring the robustness of models.
The authors focus on Vision-Language Models (VLMs), specifically CLIP, which have shown promise in OOD detection by integrating multi-modal information. However, one major challenge in practical applications of these systems is the need for manual prompt engineering. Manual prompt engineering requires domain expertise and is sensitive to linguistic nuances, making it time-consuming and error-prone.
To address this challenge, the authors propose a novel approach called Label-driven Automated Prompt Tuning (LAPT). LAPT aims to reduce the need for manual prompt engineering by developing distribution-aware prompts with in-distribution (ID) class names and negative labels mined automatically. The training samples linked to these class labels are collected autonomously through image synthesis and retrieval methods, eliminating the need for manual effort.
The LAPT framework utilizes a simple cross-entropy loss for prompt optimization. It also incorporates cross-modal and cross-distribution mixing strategies to reduce image noise and explore the intermediate space between distributions, respectively. These techniques help improve the quality of the prompts and enhance the model’s ability to distinguish between ID and OOD samples.
The key highlight of LAPT is its autonomy, as it operates without manual intervention, requiring only ID class names as input. This significantly reduces the burden on human experts and makes the process more scalable and efficient.
The authors conducted extensive experiments to evaluate the performance of LAPT. The results consistently show that LAPT outperforms manually crafted prompts, setting a new standard for OOD detection. Notably, LAPT not only enhances the distinction between ID and OOD samples but also improves ID classification accuracy and strengthens the model’s generalization robustness to covariate shifts. This makes LAPT highly effective in challenging full-spectrum OOD detection tasks.
Overall, the proposed LAPT framework presents a significant advancement in OOD detection for Vision-Language Models. By automating the prompt engineering process, LAPT reduces the reliance on manual effort and improves the efficiency and scalability of OOD detection systems. The outstanding performance demonstrated by LAPT in various experiments highlights its potential for practical applications in real-world scenarios. Researchers and practitioners interested in OOD detection should consider exploring LAPT and its code, which is available on GitHub.
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