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Found 11 papers, 2 papers with code
DFML: Decentralized Federated Mutual Learning
In the realm of real-world devices, centralized servers in Federated Learning (FL) present challenges including communication bottlenecks and susceptibility to a single point of failure.
Parametric Feature Transfer: One-shot Federated Learning with Foundation Models
This paper introduces FedPFT (Federated Learning with Parametric Feature Transfer), a methodology that harnesses the transferability of foundation models to enhance both accuracy and communication efficiency in one-shot FL.
Analysis and Validation of Image Search Engines in Histopathology
Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient matching for various purposes, ranging from triaging and diagnosis to prognosis and prediction.
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models
The VRM reduces the estimation error in ERM by replacing the point-wise kernel estimates with a more precise estimation of true data distribution that reduces the gap between data points \textbf{within each domain}.
When is a Foundation Model a Foundation Model
Recently, several studies have reported on the fine-tuning of foundation models for image-text modeling in the field of medicine, utilizing images from online data sources such as Twitter and PubMed.
Understanding Hessian Alignment for Domain Generalization
We validate the OOD generalization ability of proposed methods in different scenarios, including transferability, severe correlation shift, label shift and diversity shift.
Mathematical Challenges in Deep Learning
Deep models are dominating the artificial intelligence (AI) industry since the ImageNet challenge in 2012.
Learning Binary and Sparse Permutation-Invariant Representations for Fast and Memory Efficient Whole Slide Image Search
Learning suitable Whole slide images (WSIs) representations for efficient retrieval systems is a non-trivial task.
Beyond Neighbourhood-Preserving Transformations for Quantization-Based Unsupervised Hashing
We relax the orthogonality constraint on the projection in a PCA-formulation and regularize this by a quantization term.
Pay Attention with Focus: A Novel Learning Scheme for Classification of Whole Slide Images
The feature extractor model is fine-tuned using hierarchical target labels of WSIs, i. e., anatomic site and primary diagnosis.
A non-alternating graph hashing algorithm for large scale image search
The problem with all existing relaxation methods is resorting to one or more additional auxiliary variables to attain high quality binary codes while relaxing the problem.
