Domain-Adaptive Pretraining Methods for Dialogue UnderstandingHaisong ZhangHan WuKun XuLifeng JinLinfeng SongLinqi Song
Exploring Effectiveness of Domain and Task-Adaptive Pretraining for Clinical Information Extraction To capture domain-adaptive information, MAFR incorporates a meta-weight generator to effectively capture domain-adaptive information. Second, MAM leverages the ... V Joopudi,A Poddar,B Dandala,... 被引...
domain: While the Rare Class Sampling on the source domain improves the quality of pseudo-labels by mitigating the confirmation bias of self-training towards common classes, the Thing-Class ImageNet Feature Distance and a learning rate warmup promote feature transfer from ImageNet pretraining. DA...
Traditional deep learning-based visual imitation learning techniques require a large amount of demonstration data for model training, and the pre-trained models are difficult to adapt to new scenarios. To address these limitations, we propose a unified framework using a novel progressive learning ...
In particular, we observe that the DAFormer network is able to segment some of the classes at the beginning of the training but forgets them after a few hundred training steps as we will show in Sec. 4.5. Therefore, we assume that the us...
the source domain: While theRare Class Samplingon the source domain improves the quality of pseudo-labels by mitigating the confirmation bias of self-training towards common classes, theThing-Class ImageNet Feature Distanceand aLearning Rate Warmuppromote feature transfer from ImageNet pretraining. ...
Baseline69.973.42.67.9 Joint64.368.93.09.2 CoP75.077.58.224.6 Table 4: Component analysis experiment results for the proposed CoP method. The experiment is conducted on theMarket-1501→ MSMT-17 task. PR: color style transfer at the source domain pre-training stage; CT: color style tranfer for...
(NLP)5. Graph embedding algorithms mainly focus on the neighborhood of nodes and edges to extract knowledge, such as Netpro2vec6. However, deep learning approaches for healthcare have some limitations when training models using specific data, as the model requires large amounts of annotated ...
In this work, we propose an internal LM estimation (ILME) method to facilitate a more effective integration of the external LM with all pre-existing E2E models with no additional model training, including the most popular recurrent neural network transducer (RNN-T) a...
A training job can be launched using: python run_experiments.py --config configs/hrda/gtaHR2csHR_hrda.py The logs and checkpoints are stored in work_dirs/. For the other experiments in our paper, we use a script to automatically generate and train the configs: python run_experiments.py ...