Summary of Density Adaptive Attention Is All You Need: Robust Parameter-efficient Fine-tuning Across Multiple Modalities, by Georgios Ioannides et al.
Density Adaptive Attention is All You Need: Robust Parameter-Efficient Fine-Tuning Across Multiple Modalities
by Georgios Ioannides, Aman Chadha, Aaron Elkins
First submitted to arxiv on: 20 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD); Audio and Speech Processing (eess.AS); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Multi-Head Density Adaptive Attention Mechanism (DAAM) is a novel probabilistic attention framework for Parameter-Efficient Fine-tuning (PEFT). DAAM integrates learnable mean and variance into its attention mechanism, implemented in a multi-head framework, to collectively model any probability distribution for dynamic recalibration of feature significance. This method demonstrates significant improvements, especially with highly non-stationary data, surpassing the state-of-the-art attention techniques in model performance, up to approximately +20% (abs.) in accuracy. The DAAM is designed to enhance information aggregation across multiple modalities, including Speech, Text, and Vision. Empirically, DAAM exhibits superior adaptability and efficacy across a diverse range of tasks, including emotion recognition in speech, image classification, and text classification, thereby establishing its robustness and versatility in handling data across multiple modalities. Furthermore, the Importance Factor is introduced as a new learning-based metric that enhances the explainability of models trained with DAAM-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new attention mechanism called Multi-Head Density Adaptive Attention Mechanism (DAAM) that can be used for Parameter-Efficient Fine-tuning (PEFT). The method works by integrating learnable mean and variance into its attention mechanism, which allows it to model any probability distribution. This helps with dynamic recalibration of feature significance. The paper shows that this method performs better than other state-of-the-art methods, especially when dealing with non-stationary data. The DAAM is designed to work across multiple modalities, such as speech, text, and vision. It works well for tasks like emotion recognition in speech, image classification, and text classification. |
Keywords
* Artificial intelligence * Attention * Fine tuning * Image classification * Parameter efficient * Probability * Text classification
