conditionwang / fcil Goto Github PK
View Code? Open in Web Editor NEWThis is the formal code implementation of the CVPR 2022 paper 'Federated Class Incremental Learning'.
This is the formal code implementation of the CVPR 2022 paper 'Federated Class Incremental Learning'.
I was wondering that in the paper when you mention that you add the same number of classes in the clients to perform incremental in classes, the new classes added in the same client, are they of the same category? For e.g. suppose I have a model that predicts dog breeds. Suppose client 1 receives data on breed x and incremental learning is performed with this new data for breed and client 2 received data on breed y, and similarly incremental learning is performed for breed y, so are breed x= breed y or can they even be different breeds?
May I ask if the learning rate is 2.0 above T=5 and T=20.
why you have initialized 125 GLFC models where they are getting used( confusion is that there are only 30 clients and we have 125 models), how those 125 models are used?
May I ask if your processing of the tinyimagenet dataset is to separate 50 samples from each class in its training,and then use them as test set, which is the testing effect of tinyimagenet in your paper
Hey, I am also researching in the federated class incremental learning domain and found your paper to be very interesting. I tried running the code on A-100 80 GB machine but it throws CUDA out of memory error even with a batch size of 32 instead of 128. Can you tell me about the gpu requirements for training on tiny-imagenet-200?
In federated learning, the dataset is commonly partitioned among clients. However, I am unable to find the code related to dataset partitioning. It appears that all clients are sharing the same dataset. Could you tell me what I'm missing?
Line 282 in 4cb558a
Are you using multiple GPUs? What is the video memory of the GPU used?
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.