Task-Oriented Feature Distillation 摘要: 特征蒸馏是知识蒸馏的一种主要方法。现有的特征蒸馏方法大多数都是在Teacher Network中使用手工设计的transformation。本文提出一个新的方法:面向任务的特征蒸馏(TOFD)。 文中使用的transformation,其结构是conv layers,训练使用的loss是task loss。这样做的好处是:能捕获特征中面向...
1. Squeeze-and-Excitation Networks(SEnet) 上一篇文章中提到的《learning a Discriminative Feature Network for Semantic Segmantation 》中的CAB模块为每个特征图的通道赋予了不同的… hanyf 【论文笔记】Self-Supervised Learning Disentangled Group Representation as Feature Buqi发表于磕盐记闻 【论文笔记】AAAI2022...
9.We also use a multiple systems vote mechanism on the topic distillation subtask, and get an obvious improvement. 多检索系统的投票机制,能大幅提高主题提取子任务的性能。 10.A heartbeat message and subtask oriented fault tolerance policy were also used which can achieve the reliability. 自愿...
In this paper, we propose a method of extit{e}mbedding distillation and extit{Ta}sk-oriented extit{g}eneration (extit{eTag}) for CIL, which requires neither the exemplar nor the prototype. Instead, eTag achieves a data-free manner to train the neural networks incrementally. To prevent ...
This model first learns event detection oriented embeddings of documents through a hierarchical and supervised attention based RNN, which pays word-level attention to event triggers and sentence-level attention to those sentences containing events. It then uses the learned document embedding to enhance ...
Feature engineering plays an important role in machine learning performance, in addition to radiomic features, we have trained RF and LGBM with Histogram of Oriented Gradients (HOG) features (32 bins) under the same settings, and the test results demonstrated an inferior performance (Table 9). Ta...
[SDMGrad]Xiao, P., Ban, H., & Ji, K.Direction-oriented multi-objective learning: Simple and provable stochastic algorithms. NeurIPS, 2023. [Population-Based Training]Royer, A., Blankevoort, T., & Bejnordi, B. E.Scalarization for Multi-Task and Multi-Domain Learning at Scale. NeurIPS,...
2 and existing chemical knowledge underscores the potential of the proposed model architecture and the multi-task training scheme. Furthermore, we emphasize the importance of an open and application-oriented model evaluation system for the molecular simulation community in the era of large atomic ...
Code [Multi-Task-Transformer]: Transformer for Multi-task Learning including dense prediction problems and 3D detection on Cityscapes. [Multi-Task-Learning-PyTorch]: Multi-task Dense Prediction. [Auto-λ]: Multi-task Dense Prediction, Robotics. ...
we propose a novel distillation method named task-oriented feature distillation (TOFD) where the transformation is convolutional layers that are trained in a data-driven manner by task loss. As a result, the task-oriented information in the features can be captured and distilled to students. More...