Adaptive Real-Time Multi-Loss Function Optimization Using Dynamic Memory Fusion Framework: A Case Study on Breast Cancer Segmentation

• URL: http://arxiv.org/abs/2410.19745v1
• Date: Thu, 10 Oct 2024 11:23:04 GMT
• Title: Adaptive Real-Time Multi-Loss Function Optimization Using Dynamic Memory Fusion Framework: A Case Study on Breast Cancer Segmentation
• Authors: Amin Golnari, Mostafa Diba,
• Abstract summary: We propose a novel framework called dynamic memory fusion for adaptive multi-loss function penalizing in real-time. Experiments on breast ultrasound datasets demonstrate that the framework improves segmentation performance across various metrics.
• Score: 0.0
• License:
• Abstract: Deep learning has proven to be a highly effective tool for a wide range of applications, significantly when leveraging the power of multi-loss functions to optimize performance on multiple criteria simultaneously. However, optimal selection and weighting loss functions in deep learning tasks can significantly influence model performance, yet manual tuning of these functions is often inefficient and inflexible. We propose a novel framework called dynamic memory fusion for adaptive multi-loss function penalizing in real-time to address this. This framework leverages historical loss values data to dynamically adjust the weighting of multiple loss functions throughout the training process. Additionally, this framework integrates an auxiliary loss function to enhance model performance in the early stages. To further research horizons, we introduce the class-balanced dice loss function, designed to address class imbalance by prioritizing underrepresented classes. Experiments on breast ultrasound datasets demonstrate that the framework improves segmentation performance across various metrics. These results demonstrate the effectiveness of our proposed framework in ensuring that the model dynamically adjusts its focus to prioritize the most relevant criteria, leading to improved performance in evolving environments. The source code for our proposed methodology is publicly available on GitHub.

Related papers

• Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining [55.262510814326035]
  Existing reweighting strategies primarily focus on group-level data importance. We introduce novel algorithms for dynamic, instance-level data reweighting. Our framework allows us to devise reweighting strategies deprioritizing redundant or uninformative data.
  arXiv  Detail & Related papers  (2025-02-10T17:57:15Z)
• Feasible Learning [78.6167929413604]
  We introduce Feasible Learning (FL), a sample-centric learning paradigm where models are trained by solving a feasibility problem that bounds the loss for each training sample. Our empirical analysis, spanning image classification, age regression, and preference optimization in large language models, demonstrates that models trained via FL can learn from data while displaying improved tail behavior compared to ERM, with only a marginal impact on average performance.
  arXiv  Detail & Related papers  (2025-01-24T20:39:38Z)
• "FRAME: Forward Recursive Adaptive Model Extraction -- A Technique for Advance Feature Selection" [0.0]
  This study introduces a novel hybrid approach, the Forward Recursive Adaptive Model Extraction Technique (FRAME) FRAME combines Forward Selection and Recursive Feature Elimination to enhance feature selection across diverse datasets. The results demonstrate that FRAME consistently delivers superior predictive performance based on downstream machine learning evaluation metrics.
  arXiv  Detail & Related papers  (2025-01-21T08:34:10Z)
• Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models [4.737806982257592]
  This study proposes a knowledge distillation algorithm based on large language models and feature alignment. The proposed model performs very close to the state-of-the-art GPT-4 model in terms of evaluation indicators such as perplexity, BLEU, ROUGE, and CER.
  arXiv  Detail & Related papers  (2024-12-27T04:37:06Z)
• Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate [105.86576388991713]
  We introduce a normalized gradient difference (NGDiff) algorithm, enabling us to have better control over the trade-off between the objectives. We provide a theoretical analysis and empirically demonstrate the superior performance of NGDiff among state-of-the-art unlearning methods on the TOFU and MUSE datasets.
  arXiv  Detail & Related papers  (2024-10-29T14:41:44Z)
• Fast and Efficient Local Search for Genetic Programming Based Loss Function Learning [12.581217671500887]
  We propose a new meta-learning framework for task and model-agnostic loss function learning via a hybrid search approach. Results show that the learned loss functions bring improved convergence, sample efficiency, and inference performance on tabulated, computer vision, and natural language processing problems.
  arXiv  Detail & Related papers  (2024-03-01T02:20:04Z)
• FAStEN: An Efficient Adaptive Method for Feature Selection and Estimation in High-Dimensional Functional Regressions [7.674715791336311]
  We propose a new, flexible and ultra-efficient approach to perform feature selection in a sparse function-on-function regression problem. We show how to extend it to the scalar-on-function framework. We present an application to brain fMRI data from the AOMIC PIOP1 study.
  arXiv  Detail & Related papers  (2023-03-26T19:41:17Z)
• Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient [65.08966446962845]
  offline reinforcement learning, which aims at optimizing decision-making strategies with historical data, has been extensively applied in real-life applications. We take a step by considering offline reinforcement learning with differentiable function class approximation (DFA) Most importantly, we show offline differentiable function approximation is provably efficient by analyzing the pessimistic fitted Q-learning algorithm.
  arXiv  Detail & Related papers  (2022-10-03T07:59:42Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• AutoLoss: Automated Loss Function Search in Recommendations [34.27873944762912]
  We propose an AutoLoss framework that can automatically and adaptively search for the appropriate loss function from a set of candidates. Unlike existing algorithms, the proposed controller can adaptively generate the loss probabilities for different data examples according to their varied convergence behaviors.
  arXiv  Detail & Related papers  (2021-06-12T08:15:00Z)
• Progressive Self-Guided Loss for Salient Object Detection [102.35488902433896]
  We present a progressive self-guided loss function to facilitate deep learning-based salient object detection in images. Our framework takes advantage of adaptively aggregated multi-scale features to locate and detect salient objects effectively.
  arXiv  Detail & Related papers  (2021-01-07T07:33:38Z)

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.