-
Suggested list of courses would be:
- An introduction to deep learning **
- How to train a neural network
- Regularisation in neural networks
- Deep Bayesian neural networks
- Conv…
-
## 🚀 Feature
Extension of the methods in `ot.da.*` for regression problems (by now only classification (?)).
### Motivation
I already used `ot.da.SinkhornLpl1Transport` for domain adaptation in (…
-
As for part 3: Curating OpenVid-1M, Motion Difference module, the paper says "We introduce UniMatch [27] to evaluate optical flow score as the motion difference score to select videos with smooth move…
-
### Prerequisite
- [X] I have searched [Issues](https://github.com/open-mmlab/mmdetection/issues) and [Discussions](https://github.com/open-mmlab/mmdetection/discussions) but cannot get the expected …
-
Thank you for your excellent work. Could you please tell me which paper this code is associated with? I could not find the article titled 'Federated semi-supervised learning with marginal feature dist…
-
Hello
It seems the code consider all data as labeled data. so how unlabeled data can be feed to the network?
Thank you
-
Why the "save_feature" process saves the features of the val set, and then the "clustering" process also uses these features. But the "semi-supervised" process requires the clustering results of the l…
-
hello, I have found many framework is Semi-supervised or unsupervised? For node classification, all my data has labels, so semi-supervised or unsupervised is not applicable. Do you know of a suitable …
-
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "F:/SemicycleGAN0/Semi-supervised-segmentation-cycleGAN-master/main.py", line 87, in
…
-
- https://arxiv.org/abs/2106.09018
- 2021 ICCV
本論文では、これまでの複雑なマルチステージ手法とは対照的に、エンド・ツー・エンドの半教師付きオブジェクト検出アプローチを紹介する。
エンド・ツー・エンドの学習により、カリキュラム中に疑似ラベルの品質が徐々に向上し、より正確な疑似ラベルが得られることで、物体検出の学習に役立つ。
また、このフレ…
e4exp updated
3 years ago