Summary of How Many Classifiers Do We Need?, by Hyunsuk Kim et al.
How many classifiers do we need?by Hyunsuk Kim, Liam Hodgkinson, Ryan Theisen, Michael W. MahoneyFirst…
How many classifiers do we need?by Hyunsuk Kim, Liam Hodgkinson, Ryan Theisen, Michael W. MahoneyFirst…
Learning local discrete features in explainable-by-design convolutional neural networksby Pantelis I. Kaplanoglou, Konstantinos DiamantarasFirst submitted…
Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networksby Axel Klawonn, Martin…
Multi-Level Feature Distillation of Joint Teachers Trained on Distinct Image Datasetsby Adrian Iordache, Bogdan Alexe,…
Bayesian Optimization for Hyperparameters Tuning in Neural Networksby Gabriele OnoratoFirst submitted to arxiv on: 29…
AiSciVision: A Framework for Specializing Large Multimodal Models in Scientific Image Classificationby Brendan Hogan, Anmol…
A Combinatorial Approach to Neural Emergent Communicationby Zheyuan ZhangFirst submitted to arxiv on: 24 Oct…
Robust Visual Representation Learning with Multi-modal Prior Knowledge for Image Classification Under Distribution Shiftby Hongkuan…
Comparative Evaluation of Clustered Federated Learning Methodsby Michael Ben Ali, Omar El-Rifai, Imen Megdiche, André…
Neural Metamorphosisby Xingyi Yang, Xinchao WangFirst submitted to arxiv on: 10 Oct 2024CategoriesMain: Computer Vision…