Professor Kuo Delivered Keynote at ICDIS 2018

The first International Conference on Data Intelligence and Security (ICDIS-2018) was held at South Padre Island (SPI), Texas, USA, from April 9-10, 2018. The conference was organized by the University of Texas Rio Grande Valley (UTRGV). Key people in attendance were from the U.S. Department of Defense, the National Science Foundation, and other agencies focused on data intelligence and security.

Dr. Parwinder Grewal, UTRGV executive vice president for Research, Graduate Studies, and New Program Development, welcomed attendees to the conference in the open ceremony held on April 9 (Monday) morning. After his opening remarks, MCL Director, Professor Jay Kuo, gave the first keynote speech on the topic, “Why and Why Not Convolutional Neural Networks.” The abstract of his talk is given below.

“The superior performance of Convolutional Neural Networks (CNNs) has been demonstrated in many applications such as image classification, detection, and processing. Yet, the CNN solution has its own weaknesses such as robustness against perturbation, scalability against the class number and portability among different datasets. Furthermore, CNN’s working principle remains mysterious. In this talk, I will first explain the reasons behind the superior performance of CNNs. Then, I will present an alternative solution, which is motivated by CNNs yet allows rigorous and transparent mathematical treatment, based on a data-driven Saak (Subspace approximation with augmented kernels) transform. The kernels of the Saak transform are derived from the second-order statistics of inputs in a one-pass feedforward way. Neither data labels nor backpropagation is needed in kernel determination. The pros and cons of CNNs and multi-stage Saak transforms are compared.”

The South Padre Island (http://www.myspi.org/) is a tropical island located in the Mexico Gulf. It is famous for beautiful beaches, warm Gulf waters, fishing, boating, bird watching, and shopping. [...]

By |April 14th, 2018|News|Comments Off on Professor Kuo Delivered Keynote at ICDIS 2018|

MCL Research on CNN Incremental Learning

One fundamental problem of the convolutional neural network(CNN) is catastrophic forgetting, which occurs when new object classes and data are added while the original dataset is not available anymore. Training the network only using the new dataset deteriorates the performance with respect to the old dataset. To overcome this problem, we propose an expanded network architecture, called the ExpandNet, to enhance the CNN incremental learning capability. Our solution keeps filters of the original networks on one hand, yet adds additional filters to the convolutional layers as well as the fully connected layers on the other hand.

The proposed new architecture does not need any information of the original dataset, and it is trained using the new dataset only. Extensive evaluations based on the CIFAR-10 and the CIFAR-100 datasets show that the proposed method has a slower forgetting rate as compared to several existing incremental learning networks.

As a further extension, modifications such as pruning can be used to reduce the size of the proposed ExpandNet. Also, the Saak transform was recently proposed in [1]. It is worthwhile to compare the Saak-transform-based approach and the ExpandNet approach with respect to the new dataset.


[1] C-C Jay Kuo and Yueru Chen, “On data-driven saak transform,” arXiv preprint arXiv:1710.04176, 2017.


Image credits:

1. Image showing an illustration of the incremental learning problem.

2. Image showing the network architecture of the proposed ExpandNet, where new trainable filters added to the convolutional layers and FC layers are shown in orange while the original filters are shown in blue.


By Shanshan Cai, an alumna of MCL

By |April 8th, 2018|News|Comments Off on MCL Research on CNN Incremental Learning|

Welcome New MCL Member – Charan Shettyhalli Guruswamy

We are so happy to welcome a new Master’s member of MCL, Charan Shettyhalli Guruswamy. Here is an interview with Charan :
1. Could you briefly introduce yourself and your research interests?
I am Charan, a new grad student who joined EE dept in spring 2018. I love to work hard and think more. I got attracted to image processing and machine learning during my undergrad. Starting then I have pursued computer vision and the math behind it. My previous research included face & object detection and recognition, biometrics, NLP, optimization, ML and deep learning. I have a passion to play with numbers, and I see images as just matrices. I am not a big fan of neural networks, I like convolutions more than deep learning. I have interest in designing new transforms for data, I hope to research on scene understanding and video completion.

2. What is your impression about MCL and USC?
MCL is a group filled with image processing passionates. Every one of them has their own beautiful perspectives in analyzing the images, and their unique works enthrall me. There is a lot of math, analysis, and visualization here, that’s what I like in MCL. Weekly sessions by doctoral students help in understanding the new topics in the IP field, thus triggering motivation in all. The rich MCL alumni and community guiding the current MCL candidates is ideal for career growth. MCL helps in breaking our shell and surpass our limits.

3. What is your future expectation and plan in MCL?
My goal is to design a transforms which can extract and select features from any data. I want to learn how to model this transforms. I want to join the peer community of [...]

By |March 4th, 2018|News|Comments Off on Welcome New MCL Member – Charan Shettyhalli Guruswamy|

Welcome New MCL Member Yijing Yang!

We are so happy to welcome a new Master’s member of MCL, Yijing Yang. Here is an interview with Yijing:

1. Could you briefly introduce yourself and your research interests?

I’m Yijing Yang, currently a Master’s student in Electrical Engineering at University of Southern California. My emphasis during graduate study is signal processing. Now my interests have gradually specialized in image processing and deep learning. I hope that one day I can apply the knowledge I learnt in these areas to solving multimedia data processing and object detection problems.

2. What is your impression about MCL and USC?

MCL is enriched with knowledge, resources, and friendships. Team members always work together and are willing to help each other to move forward. I can feel the strong enthusiasm from every researcher here. I’d like to join them and make progress.

3. What is your future expectation and plan in MCL?

I hope that I can learn more about image processing and deep learning, and improve my abilities in doing research. I also hope to make new friends in the lab and make some contribution to the project as well.

By |January 14th, 2018|News|Comments Off on Welcome New MCL Member Yijing Yang!|