HuHaigen / Adaptively-Customizing-Activation-Functions
To enhance the nonlinearity of neural networks and increase their mapping abilities between the inputs and response variables, activation functions play a crucial role to model more complex relationships and patterns in the data. In this work, a novel methodology is proposed to adaptively customize activation functions only by adding very few pa…
☆16Updated last year
Alternatives and similar repositories for Adaptively-Customizing-Activation-Functions:
Users that are interested in Adaptively-Customizing-Activation-Functions are comparing it to the libraries listed below
- [TMI'22] Personalized Retrogress-Resilient Federated Learning Towards Imbalanced Medical Data☆13Updated 2 years ago
- ☆17Updated 9 months ago
- [MICCAI'21] Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning☆17Updated 3 years ago
- MultiMix: Sparingly Supervised, Extreme Multitask Learning From Medical Images (ISBI 2021, MELBA 2021)☆32Updated 2 years ago
- One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis (pFLSynth)☆25Updated last year
- [MICCAI 2023] GRACE: Enhancing Federated Learning for Medical Imaging with Generalized and Personalized Gradient Correction☆16Updated last year
- Official implementation of the paper "FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heter…☆13Updated last year
- Official Implementation of the CVPR'23 paper 'Regularization of polynomial networks for image recognition'.☆9Updated last year
- Code for MICCAI 2023 paper titled "Semi-supervised Domain Adaptive Medical Image Segmentation through Consistency Regularized Disentangle…☆20Updated 7 months ago
- [npj Digital Medicine'21] Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational val…☆39Updated 3 years ago
- Training Federated GANs with Theoretical Guarantees: AUniversal Aggregation Approach☆17Updated 4 years ago
- ☆10Updated last year
- ActiveSegmentation: A Simulation Framework for Benchmarking Active Learning Strategies for 3D Medical Image Segmentation☆20Updated 2 years ago
- ☆19Updated 3 years ago
- Official Pytorch implementation of model in "Tensor Networks for Medical Image Classification", Raghavendra Selvan & Erik Dam, MIDL 2020☆26Updated 2 years ago
- [ECCV 2022] Personalizing Federated Medical Image Segmentation☆49Updated 2 years ago
- impelmentation of https://arxiv.org/pdf/2001.05647.pdf☆34Updated 4 years ago
- [MICCAI2024] "FedFMS: Exploring Federated Foundation Models for Medical Image Segmentation". A framework for fine-tuning SAM (Segment Any…☆48Updated 2 months ago
- Patho-GAN: interpretation + medical data augmentation. Code for paper work "Explainable Diabetic Retinopathy Detection and Retinal Image …☆78Updated 3 years ago
- [AAAI 2022 Oral] Separate Contrastive Learning for Organs-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation☆15Updated 2 years ago
- Code for the paper titled "Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks" (NeurIPS…☆11Updated 3 years ago
- The source code of 'Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification' (MICCAI 2021)☆18Updated 3 years ago
- ☆18Updated last year
- ☆19Updated 2 years ago
- AsynDGAN project source code.☆42Updated 4 years ago
- Self-supervised federated learning for medical imaging - IEEE TMI☆71Updated 10 months ago
- ☆10Updated last year
- [ICML 2022] This work investigates the compatibility between label smoothing (LS) and knowledge distillation (KD). We suggest to use an L…☆11Updated 2 years ago
- ☆20Updated 3 years ago
- Hyper-Convolution Networks for Biomedical Image Segmentation☆29Updated 2 years ago