Model Compression Techniques in Biometrics Applications: A Survey

• URL: http://arxiv.org/abs/2401.10139v1
• Date: Thu, 18 Jan 2024 17:06:21 GMT
• Title: Model Compression Techniques in Biometrics Applications: A Survey
• Authors: Eduarda Caldeira, Pedro C. Neto, Marco Huber, Naser Damer, Ana F. Sequeira
• Abstract summary: Deep learning algorithms have extensively empowered humanity's task automatization capacity. The huge improvement in the performance of these models is highly correlated with their increasing level of complexity. This led to the development of compression techniques that drastically reduce the computational and memory costs of deep learning models without significant performance degradation.
• Score: 5.452293986561535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The development of deep learning algorithms has extensively empowered humanity's task automatization capacity. However, the huge improvement in the performance of these models is highly correlated with their increasing level of complexity, limiting their usefulness in human-oriented applications, which are usually deployed in resource-constrained devices. This led to the development of compression techniques that drastically reduce the computational and memory costs of deep learning models without significant performance degradation. This paper aims to systematize the current literature on this topic by presenting a comprehensive survey of model compression techniques in biometrics applications, namely quantization, knowledge distillation and pruning. We conduct a critical analysis of the comparative value of these techniques, focusing on their advantages and disadvantages and presenting suggestions for future work directions that can potentially improve the current methods. Additionally, we discuss and analyze the link between model bias and model compression, highlighting the need to direct compression research toward model fairness in future works.

Related papers

• Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique [46.266960248570086]
  We introduce an innovative offline recording strategy that avoids the simultaneous loading of both teacher and student models. This approach feeds a multitude of augmented samples into the teacher model, recording both the data augmentation parameters and the corresponding logit outputs. Experimental results demonstrate that the proposed distillation strategy enables the student model to achieve performance comparable to state-of-the-art models.
  arXiv  Detail & Related papers  (2024-09-03T16:12:12Z)
• Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models [0.0]
  An increase in the number of connected devices around the world warrants compressed models that can be easily deployed at the local devices with low compute capacity and power accessibility. We implemented both, quantization and pruning, compression techniques on popular deep learning models used in the image classification, object detection, language models and generative models-based problem statements.
  arXiv  Detail & Related papers  (2024-07-22T14:20:53Z)
• Structured Model Pruning for Efficient Inference in Computational Pathology [2.9687381456164004]
  We develop a methodology for pruning the widely used U-Net-style architectures in biomedical imaging. We empirically demonstrate that pruning can compress models by at least 70% with a negligible drop in performance.
  arXiv  Detail & Related papers  (2024-04-12T22:05:01Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• Uncovering the Hidden Cost of Model Compression [43.62624133952414]
  Visual Prompting has emerged as a pivotal method for transfer learning in computer vision. Model compression detrimentally impacts the performance of visual prompting-based transfer. However, negative effects on calibration are not present when models are compressed via quantization.
  arXiv  Detail & Related papers  (2023-08-29T01:47:49Z)
• Towards a Better Theoretical Understanding of Independent Subnetwork Training [56.24689348875711]
  We take a closer theoretical look at Independent Subnetwork Training (IST) IST is a recently proposed and highly effective technique for solving the aforementioned problems. We identify fundamental differences between IST and alternative approaches, such as distributed methods with compressed communication.
  arXiv  Detail & Related papers  (2023-06-28T18:14:22Z)
• GLUECons: A Generic Benchmark for Learning Under Constraints [102.78051169725455]
  In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. We model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints.
  arXiv  Detail & Related papers  (2023-02-16T16:45:36Z)
• What do Compressed Large Language Models Forget? Robustness Challenges in Model Compression [68.82486784654817]
  We study two popular model compression techniques including knowledge distillation and pruning. We show that compressed models are significantly less robust than their PLM counterparts on adversarial test sets. We develop a regularization strategy for model compression based on sample uncertainty.
  arXiv  Detail & Related papers  (2021-10-16T00:20:04Z)
• Powerpropagation: A sparsity inducing weight reparameterisation [65.85142037667065]
  We introduce Powerpropagation, a new weight- parameterisation for neural networks that leads to inherently sparse models. Models trained in this manner exhibit similar performance, but have a distribution with markedly higher density at zero, allowing more parameters to be pruned safely. Here, we combine Powerpropagation with a traditional weight-pruning technique as well as recent state-of-the-art sparse-to-sparse algorithms, showing superior performance on the ImageNet benchmark.
  arXiv  Detail & Related papers  (2021-10-01T10:03:57Z)
• Recovering Quantitative Models of Human Information Processing with Differentiable Architecture Search [0.3384279376065155]
  We introduce an open-source pipeline for the automated construction of quantitative models. We find that these methods are capable of recovering basic quantitative motifs from models of psychophysics, learning and decision making.
  arXiv  Detail & Related papers  (2021-03-25T16:00:23Z)
• Learning End-to-End Lossy Image Compression: A Benchmark [90.35363142246806]
  We first conduct a comprehensive literature survey of learned image compression methods. We describe milestones in cutting-edge learned image-compression methods, review a broad range of existing works, and provide insights into their historical development routes. By introducing a coarse-to-fine hyperprior model for entropy estimation and signal reconstruction, we achieve improved rate-distortion performance.
  arXiv  Detail & Related papers  (2020-02-10T13:13:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.