Comments (5)
from nri.
Thank you! I'll try your code.
from nri.
Your code does help. But I haven't got comparable results when the number of objects is 10. Do you have any advice for data generation or model training to promote the performance? By the way, here are the brief results.
method | acc (N=5) | acc (N=10) |
---|---|---|
reported | 96.0% | 75.7% |
reproduced | 94.6% | 67.6% |
from nri.
from nri.
Yeah, I set 'skip_first=True' as it makes a difference. I also set 'encoder=CNN'. Other settings are the same as described in the paper, and most of them are the default settings in the code.
I wonder if I should try more training data. Maybe 50k is not be enough for 10-object system.
from nri.
Related Issues (20)
- Questions regarding the edges that are created in the latent space
- For the type of edge in the experimental setup. HOT 1
- Error in running the simulation HOT 1
- my_softmax HOT 3
- Is it possible to learn more than 2 edge-types in unsupervised manner? HOT 4
- Undirected latent graph
- Undirected latent graph
- How to reproduce some paper results
- Where can I find the code of Eq. 12 in the paper?? HOT 2
- Compatibility with Pytorch 1.xx ?
- Sport UV dataset HOT 4
- non-interaction edge type
- Error in class MLP def forward HOT 2
- About edge_accuracy() in utils.py HOT 2
- Unsupervised learning HOT 2
- what does mean edge_type? HOT 1
- what does mean logits shape?
- Results of charge experiment
- /
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from nri.