Comments (1)
Hi, I have a question about implementation details regarding learned threshold merging.
In this line of code, you detach the generated mask which still has a gradient flow by straight through trick. In my understanding, still, the threshold can still be learned by flop loss. Is there any other reason for using a stop gradient in the mask applying to the features? Can it make models learn hard if no stop gradient is applied?
Thanks for providing wonderful work!
The gradient contribution happens in the line above.
Basically we only want the gradient flow to go through the mask, similarly to how it is done for pruning.
The lines you mention need to the 1. and 0. multiplication for the scatter reduce tricks but we don't want them to influence the backpropagation. It may be easier to think of it as if we need merge_mask = (merge_mask.detach() > 0.5).float()
after unm_mask = torch.ones_like(merge_mask) - merge_mask
.
from ltmp.
Related Issues (2)
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from ltmp.