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Hi,
Impressive work, it looks so simple.
In my view, the core of the work consists of three types of loss: Bandwidth, Sparsity, and Variance, which are referred to as `IPR`, `SNR`, and `EMD` i…
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I am using UniMatch for domain adaptation according to your above suggestions as mentioned in #55 . But I am facing problems and errors.
1. As you said we should cutmix labeled data (source domain)…
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### Describe the bug
Real text output looks like:
```
It's hard to say who is smarter between Isaac Newton and Albert Einstein, as both were incredibly intelligent individuals with unique perspe…
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In the inference, if the score is lower than stale_best_score, then the label be replaced with the label from this JSON file. How is this JSON file obtained? I also found out that without using this J…
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hello, @rxtan2
In your paper, I found you use 5 conditions to get the best result in the Polyvore dataset. Can you explain what conditions you used? Because I see in UT-Zappos50K, it has gender, cla…
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Hi Young, great work!
I was wondering if this method can perform well for the Domain Adaptation task. I have a dataset (street driving domain) that is unlabelled and would like to include it in the…
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Thanks for sharing great work and dataset!
I have two questions about paper.
First of all, I think that authors mainly follow the losses and architecture of ALBEF. But, CTP do not use the ITM loss…
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Hi,
Thanks very much for your work.
I wonder how should I modify your loss to adapting to the case of multiple positive samples per query.
For example, query.shape = (1,128), positive_keys.shape =…
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## Background
- ODQA 서베이 논문 요약: [Retrieving and Reading : A Comprehensive Survey on Open-domain Question Answering (2021)](https://arxiv.org/pdf/2101.00774.pdf)
- 추후에 나온 모델보다 성능이 낮다고 증명된 것은 선이 그어져…
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## 公告
1. 非常欢迎你对飞桨框架做贡献，我们正在运营一个组织——飞桨框架贡献者俱乐部（[Paddle Framework Contributor Club, PFCC](https://github.com/PaddlePaddle/community/tree/master/pfcc)），会通过定期分享技术知识与发布开发者主导任务的形式持续为飞桨框架做贡献。如果你有意向加入 PFCC，可…