“2022-01-24”版本间的差异

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* [http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=cchen&step=view_request&cvssid=846 cvss]
 
* [http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=cchen&step=view_request&cvssid=846 cvss]
 
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* Check the experimrnt of label clustering
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* Check the experiment of label clustering
 
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|Weida Liang
 
|Weida Liang
 
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* Conduct wav2vec test
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* Adjust andRun wav2vec+decoder model(training 40k/50k)
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* Add experiment details to paper
 
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* paper submission to Arxiv
 
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* Learning Clustering Algorithms
 
* Learning Clustering Algorithms
 
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* go on GAN experiment
 
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|Haoyu Jiang
 
|Haoyu Jiang
 
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* Complete data merge
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* Prepare group report
 
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* Comparison of audio-video baseline
 
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2022年1月24日 (一) 11:05的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • Reschedule the cycleFlow paper
  • Keep on investigation for multi-modality information fusion
  • Rewrite cycleFlow
Yunqi Cai
  • Investigate railway Bureau; the practical application scenario of intelligent inspection robot
  • Prepare a report for Gusu Lab
  • keep on intelligent sensor investigation
Lantian Li
  • Push CNCSRC
  • Submit final papers
  • Prepare hard trials paper
  • Go on hard trials paper
Ying Shi
  • investige forward attention here
  • continue on forward attention
Haoran Sun
  • some experiments on AutoVC pdf
  • more experiments for cycle loss
Chen Chen
  • Experiments on kmeans and use label for clustering
  • Experiments on # of phn kinds
  • cvss
  • Check the experiment of label clustering
Pengqi Li
  • study representation learning(self-supervised learning)
  • Using mel-spectrum and standard softmax on small data, models are trained
  • Data preprocessing
  • Visualization on a small models(mel-spectrum & MFCC)
  • Implement RELAX
Weida Liang
  • Conduct wav2vec test
  • Adjust andRun wav2vec+decoder model(training 40k/50k)
  • Add experiment details to paper
  • paper submission to Arxiv
Zixi Yan
  • Training supervised speech recognition models using wav2vec features
  • Experiments with more data sets
Sirui Li
  • Extract MFCC features for GAN
  • Learning Clustering Algorithms
  • go on GAN experiment
Haoyu Jiang
  • Complete data merge
  • Prepare group report
  • Comparison of audio-video baseline
Ruihai Hou
  • Preproess data for the training of UIS-RNN
  • Train the UIS-RNN model on small dataset
Renmiao Chen
  • Finish MI test.
  • Finish CKA test.