arXiv Machine Learning June 2, 2026 By Sakib Mohammad, Jarin Ritu, Md Sakhawat Hossain

Single-Channel Tissue Segmentation via Cross-Modal Distillation from Foundation Models

Read the original on arXiv Machine Learning →

arXiv:2606. 00928v1 Announce Type: cross Abstract: Multiplexed fluorescence microscopy improves tissue segmentation by providing complementary channels including nuclear (DAPI) and membrane (E-cadherin), that together encode richer spatial context than single-channel imaging alone.

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 Machine Learning.

arXiv Machine Learning

Jul 14

Student Capacity Moderates Knowledge Distillation Effectiveness: A Systematic Study Across ResNet Teacher-Student Pairs on CIFAR-10

arXiv:2605. 31191v2 Announce Type: replace Abstract: We investigate how teacher-student capacity relationships modulate knowledge distillation (KD) effectiveness in ResNet-based image classification on CIFAR-10.

By Umut Onur Yasar

computer-vision efficiency

arXiv AI

Jun 2

DASH: Dual-Branch Score Distillation for Guidance-Calibrated Compact Diffusion Models

arXiv:2606. 00798v1 Announce Type: cross Abstract: Parameter compression of class-conditional diffusion models reveals an underexplored limitation in output-level distillation: the unconditional score branch remains unsupervised, leaving the classifier-free guidance gap underdetermined in the student.

By Abdullah Al Shafi, Kazi Saeed Alam, Sk Imran Hossain, Engelbert Mephu Nguifo

diffusion efficiency benchmarks

arXiv Machine Learning

Jun 11

Beyond Dark Knowledge: Mixup-Based Distillation for Reliable Predictions

arXiv:2606. 12171v1 Announce Type: cross Abstract: Knowledge Distillation (KD) and mixup have proven effective at inducing smoothness in class boundaries; KD captures inherent class relationships in probability distributions, and mixup enforces them through convex combinations of inputs.

By Jos\'e Medina, Paul Honeine, Abdelaziz Bensrhair, Amnir Hadachi

efficiency

arXiv Computer Vision

Sep 14

Uni-Light: An Ultra-Lightweight Framework via Uncertainty-Aware Knowledge Distillation for Brain Tumour Segmentation

arXiv:2609.06729v2 Announce Type: replace Abstract: Accurate 3D brain tumour segmentation from multi-modal Magnetic Resonance Imaging (MRI) is essential for clinical diagnosis and treatment planning....

By Libing Kuang, Soren Salehi, Ziling Wu, Ahmad P. Tafti, Armaghan Moemeni

computer-vision efficiency multimodal benchmarks data-engineering

arXiv Computer Vision

Aug 28

Cross-Architecture Knowledge Distillation from a Vision Foundation Model to a Lightweight Visual State Space Model for Tea Leaf Disease Classification

The paper presents a method for cross‑architecture knowledge distillation from a fine‑tuned DINOv2 Vision Transformer teacher to a lightweight bidirectional Visual State Space Model (LVSSM) student for tea leaf disease classification. By addressing training‑stability issues with a progressive convolutional stem and gated selective‑scan block, the 4.45 M‑parameter student achieves a mean test accuracy of 95.41%—a 3.09‑point improvement over the teacher’s 92.32%—while using only one‑fifth of the teacher’s parameters. Ablation studies show that simple logit‑level distillation outperforms intermediate feature alignment, and the gains are specific to students that start below the teacher’s performance.

By Zibo Zhou, Zongsen Qiu, Rui Chen, Yujie Yao, Yue Zhou, Jianjun Wang

llms rag computer-vision fine-tuning efficiency safety

arXiv Computer Vision

Sep 10

Pretraining and Distillation Matter More Than Architecture Family for Label-Free Single-Cell Classification

arXiv:2609.09863v1 Announce Type: new Abstract: Choosing a deep learning architecture for label-free single-cell classification remains an open question, with microscopy benchmarks reporting conflict...

By Philip Graemer, Giuseppe Di Caprio

llms computer-vision fine-tuning efficiency benchmarks

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Student Capacity Moderates Knowledge Distillation Effectiveness: A Systematic Study Across ResNet Teacher-Student Pairs on CIFAR-10

DASH: Dual-Branch Score Distillation for Guidance-Calibrated Compact Diffusion Models

Beyond Dark Knowledge: Mixup-Based Distillation for Reliable Predictions

Uni-Light: An Ultra-Lightweight Framework via Uncertainty-Aware Knowledge Distillation for Brain Tumour Segmentation

Cross-Architecture Knowledge Distillation from a Vision Foundation Model to a Lightweight Visual State Space Model for Tea Leaf Disease Classification

Pretraining and Distillation Matter More Than Architecture Family for Label-Free Single-Cell Classification