给定一个三元组,在teacher某一层输出中存在与anchor point的距离排序关系,其思想为降低positive样本和anchor之间距离,拉大anchor和negative之间的距离,而student也是为了学习teacher输出的这个三元组相对差异关系。 【Adaptive Multi-Teacher Multi-level Knowledge Distillation】 暂不展开 三、保留多teacher的多样性 【Ensembl...
1、多个教师模型(大模型)教一个学生模型(小模型),避免单个教师模型教学生模型,导致bias 2、当有多个老师时候,学生模型可否根据自己能力和教师特点,选择性进行学习。(假设学生很聪明) 3、最好的老师不一定教出最好的学生,比如Roberta模型>bert,但是两个模型的学生模型性能却是bert高于Roberta。原因很好理解,大模型善...
We propose a multi-teacher knowledge distillation framework for compressed video action recognition to compress this model. With this framework, the model is compressed by transferring the knowledge from multiple teachers to a single small student model. With multi-teacher knowledge distillation, students...
We propose a multi-teacher knowledge distillation framework for compressed video action recognition to compress this model. With this framework, the model is compressed by transferring the knowledge from multiple teachers to a single small student model. With multi-teacher knowledge distillation, students...
the performance of quantized student model.In this paper, we propose a novel framework that lever-ages both multi-teacher knowledge distillation and net-work quantization for learning low bit-width DNNs. Theproposed method encourages both collaborative learningbetween quantized teachers and mutual ...
Incremental learning methods can learn new classes continually by distilling knowledge from the last model (as a teacher model) to the current model (as a student model) in the sequentially learning process. However, these methods cannot work for Incremental Implicitly-Refined Classification (IIRC),...
Knowledge distillation~(KD) is an effective learning paradigm for improving the performance of lightweight student networks by utilizing additional supervision knowledge distilled from teacher networks. Most pioneering studies either learn from only a single teacher in their distillation learning methods, neg...
To tackle this challenge, we propose a Two-stage Multi-teacher Knowledge Distillation (TMKD for short) method for web Question Answering system. We first develop a general Q\&A distillation task for student model pre-training, and further fine-...
Class Incremental Learning with Multi-Teacher Distillation Haitao Wen, Lili Pan, Yu Dai, Heqian Qiu, Lanxiao Wang*, Qingbo Wu, Hongliang Li* University of Electronic Science and Technology of China, Chengdu, China {haitaowen, ydai, lanxiao.wang}@std.uestc.edu.cn, {lili...
2.1 Multi-Task Refined Teacher Model 我们使用Bert作为底层的共享文本编码层,并为每种类型的NLU任务配备了一个任务特定层(Task-specific Top Layer)。 训练过程主要分为两个阶段: 1、共享层的预训练:使用 Bert 来初始化这些共享层,然后通过完形填空任务和下句预测任务进行训练。 2、多任务细化:在此阶段所有的模...