arXiv AI August 13, 2026 By Mariano Rivera, Angello Hoyos

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

arXiv:2505. 18315v3 Announce Type: replace-cross Abstract: We introduce \textbf{CoLoRA} (Convolutional Low-Rank Adaptation), a parameter-efficient fine-tuning method for convolutional neural networks (CNNs).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 2

OP-LoRA: The Blessing of Dimensionality

arXiv:2412. 10362v2 Announce Type: replace Abstract: Low-rank adapters (LoRA) enable finetuning of large models with only a small number of parameters.

By Piotr Teterwak, Kate Saenko, Bryan A. Plummer, Ser-Nam Lim

diffusion fine-tuning

arXiv AI

Sep 1

ActiveAugment: Online Active Learning for Augmentation Selection in Deep Learning

arXiv:2608.28923v1 Announce Type: cross Abstract: Data augmentation is a cornerstone of deep learning pipelines, yet existing strategies treat it as a static, model-agnostic preprocessing step, eithe...

By Noah Videcrantz, Mostafa Mehdipour Ghazi

llms fine-tuning benchmarks

arXiv Computer Vision

Sep 11

Spectral Adapters for Segment Anything Model-based Segmentation of Colorectal Liver Metastases in Computed Tomography

The paper introduces two lightweight spectral adapters—Directional Spectral Adapter (DiSECT) and Spectral Instance-Guided Adapter (SiGA)—to adapt the Segment Anything Model (SAM) for accurate segmentation of colorectal liver metastases in contrast‑enhanced CT scans. SiGA achieves the highest single‑point Dice score of 0.77 and performs comparably to a 3D nnU‑Net baseline under no‑prompt inference, while DiSECT requires only 0.14 million trainable parameters. The study evaluates the adapters on 446 CT volumes across various prompting regimes, demonstrating that spectral adapters can efficiently adapt SAM with limited trainable parameters while maintaining strong segmentation accuracy.

By Ramtin Mojtahedi, Mohammad Hamghalam, Jacob J. Peoples, Natalie Gangai, Mithat Gonen, Yun Shin Chun, HyunSeon Christine Kang, Richard K. G. Do, Amber L. Simpson

computer-vision fine-tuning

arXiv Computer Vision

Aug 24

When Adaptation Hurts: Connecting Representational Drift to OOD Failures in MedSAM Fine-Tuning

arXiv:2608.21300v1 Announce Type: new Abstract: Foundation models for medical image segmentation, like prompt-based MedSAM, generalize well across domains and modalities, often in zero or few-shot se...

By Marko Haralovi\'c, Sounic Akkaraju, Carlo Baretta, Vasil Zapryanov, Alexia Briassouli

computer-vision fine-tuning benchmarks safety

arXiv AI

Aug 11

LoRSA: Toward Generalizable Parameter-Efficient Fine-Tuning for Biomedical Downstream Tasks

arXiv:2608. 07749v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning enables the adaptation of vision foundation models to biomedical tasks under limited computational resources, but a single low-rank update can constrain all task-specific changes to one narrow parameter subspace.

By Saed Moradi, Benyamin Ghojogh, M. Hadi Sepanj, Yimin Yang, Ashirbani Saha

fine-tuning

arXiv Computer Vision

Aug 25

Extending the Horizon of Early Diagnosis: Lung Cancer Prediction with Vision Transformers

arXiv:2608.21571v1 Announce Type: new Abstract: Lung cancer remains a leading cause of cancer-related mortality worldwide, and early diagnosis is critical for improving survival. However, early-stage...

By Olivera Kotevska, Ian Goethert, Michael McGee, Maria Mahbub, Sean R. Wilkinson, Rowena Yip, Myvizhi Esai Selvan, Zeynep H. Gumus, Claudia Henschke, Robert J. Klein, Providencia Morales, Samuel M Aguayo, Ioana Danciu, Mayanka Chandrashekar

llms fine-tuning

One email a morning, machine-written

Related stories

OP-LoRA: The Blessing of Dimensionality

ActiveAugment: Online Active Learning for Augmentation Selection in Deep Learning

Spectral Adapters for Segment Anything Model-based Segmentation of Colorectal Liver Metastases in Computed Tomography

When Adaptation Hurts: Connecting Representational Drift to OOD Failures in MedSAM Fine-Tuning

LoRSA: Toward Generalizable Parameter-Efficient Fine-Tuning for Biomedical Downstream Tasks

Extending the Horizon of Early Diagnosis: Lung Cancer Prediction with Vision Transformers