Designing models for learning dataset with complex distributions is one of the main challenges that still remains in machine learning areas. We propose CollaboNet, which can divide a large dataset into sub-datasets, train two generative models separately, and let two models work together to achieve better performance. The proposed algorithm divides a large dataset without label since the capability difference between two generative models in performing tasks on each data is the main criterion for dividing a large dataset. In other words, the classification model can be trained by unsupervised manner. Autoencoder experiments for pure MNIST and the datasets combined artificially from two image sets shows that CollaboNet successfully splits large datasets without labels, improving the performance of generative models.
|Title of host publication||2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|Publication status||Published - 2018 Aug 29|
|Event||25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece|
Duration: 2018 Oct 7 → 2018 Oct 10
|Name||Proceedings - International Conference on Image Processing, ICIP|
|Conference||25th IEEE International Conference on Image Processing, ICIP 2018|
|Period||18/10/7 → 18/10/10|
Bibliographical notePublisher Copyright:
© 2018 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Signal Processing