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Recently, Professor Kuo and his students at MCL proposed a new machine learning methodology called successive subspace learning (SSL). The methodology has been widely adopted in MCL to solve image processing and computer vision problems. In 3D domain, we have observed a great success in point cloud classification task. In the PointHop paper, we develop an explainable machine learning method for point cloud classification. The classification baseline is composed by four PointHop units, we construct the local-to-global attribute building process and use saab transform to control the dimension growth in each unit. We compare the test performance on ModelNet40 with the state-of-the-art methods, our method obtains comparable performance with the others while demands much less training time. For instance, PointNet costs about 5 hours to train, while ours only takes 20 minutes to train on the same dataset. The advantages of the methodology are very clear: interpretable and much less computation complexity.

The success in point cloud classification encourages us to go deeper in 3D domain. Therefore, we further look at the segmentation task which needs to assign label to each point in the point cloud. Referring to the common image segmentation network, we use the point cloud classification baseline as an encoder and add a decoder to complete segmentation. After building local neighboring regions and extracting local attributes from neighboring points in the encoder, the features are interpolated back to finest scale layer by layer with skip connections between same scales in the decoder. Also, saab transform is adopted between layers as feedfoward convolution to control the rapid growth of the feature dimension.

Our method also has the advantage of task-agnostic ability. Specifically, by learning the parameters in a one-pass manner, our [...]

We are so happy to welcome a new undergraduate member of MCL, Eric Huang. Here is an interview with Eric:

Could you briefly introduce yourself and your research interests?

Hi, my name is Eric Huang and I’m a current senior studying computer science at USC. I’ve been interested in research ever since I started college and the MCL is a lab I’m honored to have been able to join. I previously worked in biomedical and mechanical engineering labs, but machine learning is the field I am most interested in. My research focus is on facial classification and verification, particularly in studying how to streamline the training process. But I think that every new development in AI is incredible and I hope to be able to learn and understand the many aspects.

What is your impression about MCL and USC?

My impression of USC after three years is that the school is amazing. There are countless intelligent students and professors that form a productive and supportive network. I see the MCL as the pinnacle of what USC can achieve. Everyone in the MCL is highly driven, intelligent, and passionate and I hope to live up to the same standard.

What is your future expectation and plan in MCL?

My expectation and plan in the MCL are to learn, grow, and hopefully contribute. I plan to study hard and learn everything I can in order to be able to meaningfully help out. I expect to learn a lot from the lab members and to work hard supporting them. And I hope that I am able to form lasting connections with all the talented people I get to work with.

We are so happy to welcome a new graduate member of MCL, Xiou Ge. Here is an interview with Xiou:

Could you briefly introduce yourself and your research interests?

My name is Xiou Ge. I’m a first year PhD student in Electrical Engineering. I’m from Harbin, China. After completing my middle school education in China, I was awarded a scholarship and spend my high school years in Singapore. I obtained my bachelor’s degree with highest honors and the master’s degree, both in Electrical Engineering from the University of Illinois at Urbana-Champaign. Previously, I interned at Apple in Cupertino and worked on EDA tool development, and IBM T.J. Watson Research Center in Yorktown Heights and did computational creativity research. My current research interests include artificial intelligence, machine learning, and computer vision.

What is your impression about MCL and USC?

Everyone I met in MCL has been very nice and willing to help each other. The atmosphere is very relaxed and people interact with each other just like family members. Yet everybody works very hard and I can feel the energy which motivates me to work harder. I think MCL has the conducive environment for me to become a successful graduate student. USC offers a very different college experience from my previous one. Located in the middle of downtown LA, the school offers plenty of opportunities for students to experience and learn from the outside world. Collaborations with researchers from other institutions and other parts of the world are frequent and convenient. The school also makes substantial investment in education and allows students to learn from and do research with world-class faculty and experts in different research fields. 

What is your future expectation and plan in [...]

We are so happy to welcome a new graduate member of MCL, Hongyu Fu. Here is an interview with Hongyu:

1. Could you briefly introduce yourself and your research interests?
My name is Hongyu Fu, before becoming a PhD student in USC, I got my bachelor’s degree in electrical engineering from Peking University. My past research experience focuses mostly on semiconductor device physics and circuits, while exploring device and circuit based neuromorphic computing and machine learning topics, I have heard more and more about computer vision, AI and machine learning, which are always my keen interest that I haven’t got chances to learn before. Therefore, I really appreciate Prof. Kuo for giving me this opportunity to study in MCL and explore more in this exciting area.
 
2. What is your impression about MCL and USC?
I really love the beautiful campus, friendly atmosphere and enjoy the convenient facilities of USC. We have a fight-on spirit as Trojans, which makes USC a great network and everyone in this network passionate and motivated. MCL is a large and efficient group with a hard-working spirit and intelligent students with solid skills and deep knowledge under the supervision of Prof. Kuo, who is a very nice and responsible advisor profound in knowledge and research.
 

3. What is your future expectation and plan in MCL?
I will definitely work hard, learn solid skills of math, problems solving and research and hope to contribute to MCL in the future. I wish that in the near future, the MCL group and myself would have a solid standing in the machine learning and computer vision community and contribute to the improvement of this field.

The thesis is entitled “Object Classification Based on Neural-Network-Inspired Image Transforms”.

Abstract of the thesis:

Convolutional neural networks (CNNs) have recently demonstrated impressive performance in image classification and change the way building feature extractors from carefully handcrafted design to automatically deep learned from a large labeled dataset. However, a great majority of current CNN literature are application-oriented, and there is no clear understanding and theoretical foundation to explain the outstanding performance and indicate the way to improve. In this thesis, we focus on solving the image classification problem-based on the neural-network-inspired transforms.

Being motivated by the multilayer RECOS (REctified-COrrelations on a Sphere) transform, two data-driven signal transforms are proposed, called the “Subspace approximation with augmented kernels” (Saak) transform and “Subspace approximation with adjusted bias” (Saab) transform corresponding to each Convolutional layers in CNNs. Based on the Saak transform, We firstly proposed an efficient, scalable and robust approach to the handwritten digits recognition problem. Next, we also develop an ensemble method using Saab transform to solve the image classification problem. The ensemble method fuses the output decision vectors of Saab-transform-based decision system. To enhance the performance of the ensemble system, it is critical to increasing the diversity of FF-CNN models. To achieve this objective, we introduce diversities by adopting three strategies: 1) different parameter settings in convolutional layers, 2) flexible feature subsets fed into the Fully-connected (FC) layers, and 3) multiple image embeddings of the same input source.  We also extend our ensemble method to semi-supervised learning. Since unlabeled data may not always enhance semi-supervised learning, we define an effective quality score and use it to select a subset of unlabeled data in the training process. In the last, we proposed a unified framework, called successive subspace learning [...]