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dmt-voc-106-1lr__p0--l: 80.2861213684082 dmt-voc-106-1lr__p0--r: 79.29482460021973 dmt-voc-106-1lr__p1--r: 78.88628840446472 dmt-voc-106-1lr__p1--l: 79.12319898605347 dmt-voc-106-1lr__p2--r: 78.86956334114075 dmt-voc-106-1lr__p2--l: 79.26352620124817 dmt-voc-106-1lr__p3--r: 78.67986559867859 dmt-voc-106-1lr__p3--l: 78.97126078605652 dmt-voc-106-1lr__p4--r: 78.0062735080719 dmt-voc-106-1lr__p4--l: 78.25124263763428 dmt-voc-106-1lr__p5--r: 78.01116108894348 dmt-voc-106-1lr__p5--l: 77.23076343536377
The text was updated successfully, but these errors were encountered:
@dqq813 对于自己的数据集,这是有可能的。pseudo label相关方法,尤其在有标注数据很少时,泛化性不一定好。
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可能要调整一下超参数
用有标签数据初始化两个模型时,Loss在2.0左右稳定。然而在迭代过程中,loss只有0.01左右,且一直震荡。是不是说明两个模型预测伪标签的差异很小,模型之间没有分歧,所以就不能利用模型之间的分歧去纠正伪标签。
有可能,你可以先试试普通的自训练,能不能带来提升
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dmt-voc-106-1lr__p0--l: 80.2861213684082
dmt-voc-106-1lr__p0--r: 79.29482460021973
dmt-voc-106-1lr__p1--r: 78.88628840446472
dmt-voc-106-1lr__p1--l: 79.12319898605347
dmt-voc-106-1lr__p2--r: 78.86956334114075
dmt-voc-106-1lr__p2--l: 79.26352620124817
dmt-voc-106-1lr__p3--r: 78.67986559867859
dmt-voc-106-1lr__p3--l: 78.97126078605652
dmt-voc-106-1lr__p4--r: 78.0062735080719
dmt-voc-106-1lr__p4--l: 78.25124263763428
dmt-voc-106-1lr__p5--r: 78.01116108894348
dmt-voc-106-1lr__p5--l: 77.23076343536377
The text was updated successfully, but these errors were encountered: