Learning Semantic Proxies for Efficient Fine-Tuning in Deep Metric Learning

Основні поняття

Efficient fine-tuning in deep metric learning is achieved through the use of semantic proxies.

Анотація

The paper explores parameter-efficient methods for fine-tuning pre-trained models in deep metric learning tasks. It introduces a novel framework based on learning Visual Prompts (VPT) in pre-trained Vision Transformers (ViT). By incorporating semantic information from input images and ViT, the framework optimizes visual prompts for each class. This approach improves metric learning performance by tuning only a small percentage of total parameters. The study compares various parameter-efficient strategies and demonstrates the effectiveness of the proposed method through extensive experiments on popular DML benchmarks.

Статистика

Full Fine-tuning requires 30.2M parameters.
Linear Prob has 0.19M tunable parameters.
Adapter with L:7,d:256 has 1.57M tunable parameters.
VPT with L:12,N:10 has 0.23M tunable parameters.
VPTSP-M (ours) has 0.60M tunable parameters.

Цитати

"Our new approximations with semantic information are superior to representative capabilities, thereby improving metric learning performance."
"We propose an effective learning framework based on the VPT for fine-tuning pre-trained ViTs on DML tasks."
"Our technique outperforms the original proxy-based loss regarding learning efficiency and metric learning performance."

Ключові висновки, отримані з

Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning

by Li Ren,Chen ... о arxiv.org 03-18-2024

https://arxiv.org/pdf/2402.02340.pdf

Глибші Запити

How can the proposed framework be adapted to other domains beyond computer vision

The proposed framework based on semantic proxies for fine-tuning in deep metric learning can be adapted to other domains beyond computer vision by leveraging the underlying principles and methodologies. Here are some ways this framework could be applied to different domains:

Natural Language Processing (NLP): In NLP tasks, such as text classification or sentiment analysis, semantic proxies could be used to enhance the representation of textual data. By incorporating class-based prompts and recurrent accumulation mechanisms similar to those used in image data, the model can learn more robust embeddings for text.

Speech Recognition: Semantic proxies can also be utilized in speech recognition tasks where audio data is processed. By adapting the concept of visual prompts to audio features and designing a system that optimizes soft prompts specific to different classes or categories of speech patterns, improved performance in speech recognition models can be achieved.

Healthcare: In healthcare applications like disease diagnosis or patient monitoring, semantic proxies could help improve feature representations from medical imaging data like MRI scans or X-rays. The framework could assist in better understanding complex medical images and extracting meaningful information for diagnostic purposes.

Finance: For financial applications such as fraud detection or risk assessment, semantic proxies can aid in capturing intricate patterns within financial datasets. By fine-tuning pre-trained models with class-specific prompts derived from financial indicators or transactional data, more accurate predictions and insights can be obtained.

By adapting the core concepts of semantic proxies and their integration into deep learning frameworks across various domains, it is possible to enhance model performance and achieve parameter-efficient fine-tuning strategies tailored to specific types of data.

What potential drawbacks or limitations might arise from relying heavily on semantic proxies for fine-tuning

Relying heavily on semantic proxies for fine-tuning may introduce certain drawbacks or limitations:

Overfitting: Depending too much on semantic proxies without proper regularization techniques may lead to overfitting issues where the model performs well on training data but fails to generalize effectively on unseen examples.

Semantic Gap: Semantic proxies might not always capture all nuances present in the underlying dataset due to inherent biases during training or limited representational capacity.

Increased Complexity: Managing a large number of class-based prompts along with recurrent accumulation mechanisms adds complexity both computationally and algorithmically.

Data Dependency: The effectiveness of semantic proxy-based approaches heavily relies on having sufficient labeled training data available for each class; otherwise, it may struggle with generalization when faced with new classes during inference.

How could the concept of semantic proxies be applied to non-image data in machine learning applications

The concept of using semantic proxies can be extended beyond image data into non-image machine learning applications by adapting it creatively:

Textual Data:

In natural language processing tasks like document classification or sentiment analysis, prompt tuning methods akin to visual prompts could enhance word embeddings based on contextual information.

2 .Time Series Data:

For time series forecasting applications such as stock price prediction or weather forecasting, integrating temporal cues into prompt tuning mechanisms could improve feature representations over time steps.
3 .Graph Data:

When dealing with graph structures like social networks or recommendation systems, embedding nodes using semantics learned through proxy optimization techniques would enable better node similarity calculations.
By applying the principles behind semantic proxy-based approaches creatively across diverse types of non-image datasets while considering domain-specific characteristics, significant improvements in model performance and efficiency can be achieved within these machine learning applications."