COLORA: Efficient Fine-Tuning for Convolutional Models with a Study Case on Optical Coherence Tomography Image Classification

• URL: http://arxiv.org/abs/2505.18315v1
• Date: Fri, 23 May 2025 19:21:01 GMT
• Title: COLORA: Efficient Fine-Tuning for Convolutional Models with a Study Case on Optical Coherence Tomography Image Classification
• Authors: Mariano Rivera, Angello Hoyos,
• Abstract summary: We introduce the Convolutional Low-Rank Adaptation (CoLoRA) method to overcome the inefficiencies found in current CNN fine-tuning methods.<n>We demonstrate the capabilities of our method by developing and evaluating models using the widely adopted CNN backbone pre-trained on ImageNet.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We introduce the Convolutional Low-Rank Adaptation (CoLoRA) method, designed explicitly to overcome the inefficiencies found in current CNN fine-tuning methods. CoLoRA can be seen as a natural extension of the convolutional architectures of the Low-Rank Adaptation (LoRA) technique. We demonstrate the capabilities of our method by developing and evaluating models using the widely adopted CNN backbone pre-trained on ImageNet. We observed that this strategy results in a stable and accurate coarse-tuning procedure. Moreover, this strategy is computationally efficient and significantly reduces the number of parameters required for fine-tuning compared to traditional methods. Furthermore, our method substantially improves the speed and stability of training. Our case study focuses on classifying retinal diseases from optical coherence tomography (OCT) images, specifically using the OCTMNIST dataset. Experimental results demonstrate that a CNN backbone fine-tuned with CoLoRA surpasses nearly 1\% in accuracy. Such a performance is comparable to the Vision Transformer, State-space discrete, and Kolmogorov-Arnold network models.

Related papers

• Distillation-Supervised Convolutional Low-Rank Adaptation for Efficient Image Super-Resolution [19.22142805041799]
  Convolutional neural networks (CNNs) have been widely used in efficient image super-resolution.<n>We propose Distillation-Supervised Convolutional Low-Rank Adaptation (DSCLoRA), which improves model performance without increasing architectural complexity or inference costs.
  arXiv  Detail & Related papers  (2025-04-15T15:12:57Z)
• DIPLI: Deep Image Prior Lucky Imaging for Blind Astronomical Image Restoration [2.7523980737007414]
  Astrophotography presents unique challenges for deep learning due to limited training data.<n>This work explores hybrid strategies, such as the Deep Image Prior (DIP) model, which facilitates blind training but is susceptible to overfitting.<n>We propose enhancements to the DIP model's baseline performance through several advanced techniques.
  arXiv  Detail & Related papers  (2025-03-20T09:33:16Z)
• A-SDM: Accelerating Stable Diffusion through Model Assembly and Feature Inheritance Strategies [51.7643024367548]
  Stable Diffusion Model is a prevalent and effective model for text-to-image (T2I) and image-to-image (I2I) generation. This study focuses on reducing redundant computation in SDM and optimizing the model through both tuning and tuning-free methods.
  arXiv  Detail & Related papers  (2024-05-31T21:47:05Z)
• Contrastive-Adversarial and Diffusion: Exploring pre-training and fine-tuning strategies for sulcal identification [3.0398616939692777]
  Techniques like adversarial learning, contrastive learning, diffusion denoising learning, and ordinary reconstruction learning have become standard. The study aims to elucidate the advantages of pre-training techniques and fine-tuning strategies to enhance the learning process of neural networks.
  arXiv  Detail & Related papers  (2024-05-29T15:44:51Z)
• Low-resolution Prior Equilibrium Network for CT Reconstruction [3.5639148953570836]
  We present a novel deep learning-based CT reconstruction model, where the low-resolution image is introduced to obtain an effective regularization term for improving the networks robustness. Experimental results on both sparse-view and limited-angle reconstruction problems are provided, demonstrating that our end-to-end low-resolution prior equilibrium model outperforms other state-of-the-art methods in terms of noise reduction, contrast-to-noise ratio, and preservation of edge details.
  arXiv  Detail & Related papers  (2024-01-28T13:59:58Z)
• A-SDM: Accelerating Stable Diffusion through Redundancy Removal and Performance Optimization [54.113083217869516]
  In this work, we first explore the computational redundancy part of the network. We then prune the redundancy blocks of the model and maintain the network performance. Thirdly, we propose a global-regional interactive (GRI) attention to speed up the computationally intensive attention part.
  arXiv  Detail & Related papers  (2023-12-24T15:37:47Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• ARHNet: Adaptive Region Harmonization for Lesion-aware Augmentation to Improve Segmentation Performance [61.04246102067351]
  We propose a foreground harmonization framework (ARHNet) to tackle intensity disparities and make synthetic images look more realistic. We demonstrate the efficacy of our method in improving the segmentation performance using real and synthetic images.
  arXiv  Detail & Related papers  (2023-07-02T10:39:29Z)
• Accelerating Multiframe Blind Deconvolution via Deep Learning [0.0]
  Ground-based solar image restoration is a computationally expensive procedure. We propose a new method to accelerate the restoration based on algorithm unrolling. We show that both methods significantly reduce the restoration time compared to the standard optimization procedure.
  arXiv  Detail & Related papers  (2023-06-21T07:53:00Z)
• Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond [62.730497582218284]
  We develop a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data.
  arXiv  Detail & Related papers  (2020-04-30T03:23:45Z)
• Cascaded Deep Video Deblurring Using Temporal Sharpness Prior [88.98348546566675]
  The proposed algorithm mainly consists of optical flow estimation from intermediate latent frames and latent frame restoration steps. It first develops a deep CNN model to estimate optical flow from intermediate latent frames and then restores the latent frames based on the estimated optical flow. We show that exploring the domain knowledge of video deblurring is able to make the deep CNN model more compact and efficient.
  arXiv  Detail & Related papers  (2020-04-06T09:13:49Z)

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.