Comments (9)
Hello @jchhuang , thank you very much for the reply. Could you please be a little bit more specific on where do you think is having problems?
from openlongtailrecognition-oltr.
In equation(7), both the input to the two parts of loss function is vn(meta), I think it is wrong.
from openlongtailrecognition-oltr.
@jchhuang Actually it is correct. The cross entropy loss and large margin loss should both be applied on to the output from the meta embedding. Because we use meta embedding to do the final classification. About your problem of training, as long as the losses are calculated with hallucinator based on the outputs of hallucinator, in pytorch, they will be updated using loss.step()
.
from openlongtailrecognition-oltr.
In my opinion, there is no directly relationship between centroid and vmeta, so I think it is inappropriate to express the second part as lamda* LLm(vnmeta, {ci}). I think the vnmeta here should be replaced by vdirect.
from openlongtailrecognition-oltr.
Actually that is not right, the input of center loss is processed meta embedding. In the code it more clear: https://github.com/zhmiao/OpenLongTailRecognition-OLTR/blob/master/run_networks.py#L162
We are learning centerloss regularized metric on the final meta embedding space. The centers were only initialized using direct embedding in the beginning of stage 2.
from openlongtailrecognition-oltr.
Thanks for your reply. I will reconsider this issue. thanks again
from openlongtailrecognition-oltr.
No problem. I will close this issue for now. Thanks.
from openlongtailrecognition-oltr.
I have a question:the ci in equation (9) is the centroid of vdirect or vmeta? Since only the centroid of vdirect is mentioned in the paper, so I think the computation of distance of centroid and vmeta do not make sense.
from openlongtailrecognition-oltr.
Like what I mentioned before, the centroids were only initialized using vdirect. After that the centroids are calculated using vmeta. So the equation (9) is also correct.
from openlongtailrecognition-oltr.
Related Issues (20)
- Reproducing OLTR results HOT 3
- Stage 2 multi GPU
- why fix all parameters except self attention parameters? HOT 4
- Table 2 results HOT 2
- Pretrained Weights for Places_LT?
- the use of fc layer HOT 2
- the accuracy of the train and val HOT 2
- how to compute centroids?
- Why the input dimension of the `fc_spatial` layer in `ModulatedAttLayer` is 7*7*in_channel? HOT 1
- Many_shot_accuracy_top1: nan on my own dataset HOT 1
- Revised F-measure results for other models in your paper
- Applications for face recognition
- Error when running stage_1.py under Places_LT
- Unable to reproduce baseline result on ImageNet-LT HOT 1
- BUG: stage1 test error!!
- Could you please give me an example of arranging ILSVRC2014 dataset? HOT 7
- Implementation on Inat-18
- About Class aware sampler
- The role of untrained FC(add_fc)
- The question about the version of Places_LT
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 openlongtailrecognition-oltr.