源代码:github.com/clovaai/attention-feature-distillation 编辑:牛涛 文章argue了现有研究在利用intermediate feature蒸馏时人为的设置学生和教师对齐的点,可能会存在强制让学生学根本学不会的特征。本文利用self-attention机制解决这一问题。 如上图,对学生和教师的特征图做一系列操作后得到q和k,利用q和k之间的相似性...
On this basis, we proposed the knowledge distillation method based on attention and feature transfer (AFT-KD). First, we use transformation structures to transform intermediate features into attentional and feature block (AFB) that contain both inference process information and inference outcome ...
The attention-transfer-based feature distillation step to train the student network is required to match the attention maps of the student model with the teacher model. In the next step, the pretrained student model 𝑀(𝑠𝑡;𝜙𝑠𝑡)M(st;ϕst) optimizes the loss function described ...
3. Discussion 整篇论文的精华在于 1 个简单的操作:对 teacher 的 feature map 做 normalzie !因为网络通过平均池化后的值的分布来完成分类,因此幅值对于分类结果至关重要。通过 normalzie ,feature map 的含义进化为了注意力分布。 而有趣的是,网路仅仅通过学习注意力分布,也就是该看哪些地方,不该看哪些地方,就...
[106] branded it with a new name, knowledge distillation, by introducing a temperature parameter (like chemical distillation) to scale the posteriors. In the context of E2E modeling, the token-level loss function of T/S learning is IV. OTHER TRAINING CRITERION In addition to the standard ...
Due to the quadratic complexity of the attention module with regard to token length, global attention is inefficient on large token lengths with high-resolution image inputs as discussed in the paperTraining Data-Efficient Image Transformers and Distillation Through Attention. ...
Class token and knowledge distillation for multi-head self-attention speaker verification systems 2022 The Author(s)This paper explores three novel approaches to improve the performance of speaker verification (SV) systems based on deep neural networks ... V Mingote,A Miguel,A Ortega,... - 《Dig...
FusionDTA: Attention-based feature polymerizer and knowledge distillation for drug-target binding affinity prediction. Brief. Bioinform. 2022, 23, bbab506. [Google Scholar] [CrossRef] Davis, M.I.; Hunt, J.P.; Herrgard, S.; Ciceri, P.; Wodicka, L.M.; Pallares, G.; Hocker, M.; ...
recognition, relation extraction, and question answering systems, to further enhance model performance. Moreover, BlueBERT achieves significant performance improvements across different biomedical text mining tasks by integrating weights from multiple pre-trained models using a method called model distillation....
UDKE combines dense connections, knowledge distillation, and upsampling to increase the model performance. The dense connections help to improve feature reuse and preserve more information during training. Finally, upsampling is utilized to improve the feature map resolution, which can improve segmentation...