wannabeog / mas-pytorch Goto Github PK
View Code? Open in Web Editor NEWA PyTorch implementation of the ECCV 2018 publication "Memory Aware Synapses: Learning what (not) to forget"
License: BSD 2-Clause "Simplified" License
A PyTorch implementation of the ECCV 2018 publication "Memory Aware Synapses: Learning what (not) to forget"
License: BSD 2-Clause "Simplified" License
Could you tell me how to apply Local MAS?
hi, is the classification_head right in utils/model_class?
class classification_head(nn.Module):
"""
Each task has a seperate classification head which houses the features that
are specific to that particular task. These features are unshared across tasks
as described in section 5.1 of the paper
"""
def __init__(self, in_features, out_features):
super(classification_head, self).__init__()
self.fc = nn.Linear(in_features, out_features)
def forward(self, x):
return x
When executing main.py, I only find this
The training process on the 0 tasks is completed
Testing the model now
and it crash
WHY?
Hello,
I have a question regarding the freeze layers here. I notice that the frozen layers are set to require gradient, in contrast, the not frozen layers don't require gradient. I'm not sure if I understand correctly, but this looks like we actually train frozen layers and ignore the not frozen layers, which is a little unnatural to me. Besides, we still initialize omega for not frozen layers here. Do I miss something? Thanks in advance for your help!
In the file model_train.py
, in the training loop, I see below code in L175-179:
output = model.tmodel(input_data)
_, preds = torch.max(output, 1)
running_corrects += torch.sum(preds == labels.data)
loss = model_criterion(output, labels)
What I understood is model.tmodel is the feature extractor, but yet we are comparing the outputs with target labels. Shouldn't we compare it with the last layer (classifier) output?
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.