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关于样本分配的顺序问题(或者说训练为什么可以收敛?) #113
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又思考了下这个问题,其实这个和DETR的样本分配差不多,训练一开始,没有先验,收敛可能会受到阻碍,但一直训练下去,最终还是能收敛的。网络最终自己会学习到和目标位置相关的表示,训练时手工assignment可能没必要。 手工设计的assignment可能就是不如网络自己学习到的表示,而且还不够end-to-end. |
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感谢您的工作,这个问题想请教下:
IAM去预测100个实例,但是样本分配时直接使用cost来匹配,也就是说没有基于位置的先验,那IAM是如何决定预测的顺序的呢?具体哪些通道去预测图片的哪个目标,这个是如何决定的呢? 或者换个角度讲,训练一开始 , 用来二分匹配的cost是随机的,这导致IAM一开始以随机的顺序去学习,等到下个epoch时,IAM学习的顺序就可能变了,这样的话模型也能顺利收敛吗?
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