Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settings

• URL: http://arxiv.org/abs/2403.06631v1
• Date: Mon, 11 Mar 2024 11:41:30 GMT
• Title: Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settings
• Authors: Georgios Tsoumplekas, Vladislav Li, Ilias Siniosoglou, Vasileios Argyriou, Sotirios K. Goudos, Ioannis D. Moscholios, Panagiotis Radoglou-Grammatikis, Panagiotis Sarigiannidis
• Abstract summary: This paper presents a finetuning approach to adapt standard object detection models to downstream tasks. Case study and evaluation of the energy demands of the developed models are presented. Finally, this paper introduces a novel way to quantify this trade-off through a customized Efficiency Factor metric.
• Score: 6.611985866622974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the ever-evolving era of Artificial Intelligence (AI), model performance has constituted a key metric driving innovation, leading to an exponential growth in model size and complexity. However, sustainability and energy efficiency have been critical requirements during deployment in contemporary industrial settings, necessitating the use of data-efficient approaches such as few-shot learning. In this paper, to alleviate the burden of lengthy model training and minimize energy consumption, a finetuning approach to adapt standard object detection models to downstream tasks is examined. Subsequently, a thorough case study and evaluation of the energy demands of the developed models, applied in object detection benchmark datasets from volatile industrial environments is presented. Specifically, different finetuning strategies as well as utilization of ancillary evaluation data during training are examined, and the trade-off between performance and efficiency is highlighted in this low-data regime. Finally, this paper introduces a novel way to quantify this trade-off through a customized Efficiency Factor metric.

Related papers

• A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences [21.38368444137596]
  This study introduces the High-Utility Sequential Pattern Mining using SHAP values (HUSPM-SHAP) model. Our findings demonstrate the model's capability to refine e-commerce data processing, steering towards more streamlined, cost-effective prediction modeling.
  arXiv  Detail & Related papers  (2024-10-09T10:44:02Z)
• A Closer Look at Data Augmentation Strategies for Finetuning-Based Low/Few-Shot Object Detection [5.434078645728145]
  This paper examines both model performance and energy efficiency of custom data augmentations and automated data augmentation selection strategies. It is shown that in many cases, the performance gains of data augmentation strategies are overshadowed by their increased energy usage.
  arXiv  Detail & Related papers  (2024-08-20T15:29:56Z)
• Applying Fine-Tuned LLMs for Reducing Data Needs in Load Profile Analysis [9.679453060210978]
  This paper presents a novel method for utilizing fine-tuned Large Language Models (LLMs) to minimize data requirements in load profile analysis. A two-stage fine-tuning strategy is proposed to adapt a pre-trained LLM for missing data restoration tasks. We demonstrate the effectiveness of the fine-tuned model in accurately restoring missing data, achieving comparable performance to state-of-the-art models such as BERT-PIN.
  arXiv  Detail & Related papers  (2024-06-02T23:18:11Z)
• Data Quality Aware Approaches for Addressing Model Drift of Semantic Segmentation Models [1.6385815610837167]
  This study investigates two prominent quality aware strategies to combat model drift. The former leverages image quality assessment metrics to meticulously select high-quality training data, improving the model robustness. The latter makes use of learned vectors feature from existing models to guide the selection of future data, aligning it with the model's prior knowledge.
  arXiv  Detail & Related papers  (2024-02-11T18:01:52Z)
• Data-efficient Large Vision Models through Sequential Autoregression [58.26179273091461]
  We develop an efficient, autoregression-based vision model on a limited dataset. We demonstrate how this model achieves proficiency in a spectrum of visual tasks spanning both high-level and low-level semantic understanding. Our empirical evaluations underscore the model's agility in adapting to various tasks, heralding a significant reduction in the parameter footprint.
  arXiv  Detail & Related papers  (2024-02-07T13:41:53Z)
• 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)
• Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes [72.75421975804132]
  Learning Active Learning (LAL) suggests to learn the active learning strategy itself, allowing it to adapt to the given setting. We propose a novel LAL method for classification that exploits symmetry and independence properties of the active learning problem. Our approach is based on learning from a myopic oracle, which gives our model the ability to adapt to non-standard objectives.
  arXiv  Detail & Related papers  (2023-09-11T14:16:37Z)
• A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
  This study seeks to address the demands of high-performance machine learning models with environmental sustainability. Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance. However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
  arXiv  Detail & Related papers  (2023-07-01T15:18:00Z)
• Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning [109.74041512359476]
  We study a number of design decisions for the predictive model in visual MBRL algorithms. We find that a range of design decisions that are often considered crucial, such as the use of latent spaces, have little effect on task performance. We show how this phenomenon is related to exploration and how some of the lower-scoring models on standard benchmarks will perform the same as the best-performing models when trained on the same training data.
  arXiv  Detail & Related papers  (2020-12-08T18:03:21Z)
• Goal-Aware Prediction: Learning to Model What Matters [105.43098326577434]
  One of the fundamental challenges in using a learned forward dynamics model is the mismatch between the objective of the learned model and that of the downstream planner or policy. We propose to direct prediction towards task relevant information, enabling the model to be aware of the current task and encouraging it to only model relevant quantities of the state space. We find that our method more effectively models the relevant parts of the scene conditioned on the goal, and as a result outperforms standard task-agnostic dynamics models and model-free reinforcement learning.
  arXiv  Detail & Related papers  (2020-07-14T16:42:59Z)

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.