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We are so glad to welcome our new MCL member, Hamza Ghani! Here is a short interview with Tian:

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

My name is Hamza Ghani and I am from Austin, Texas. I am a graduate student here at USC pursuing a Masters in Electrical Engineering. I went to UT Austin for my undergrad which was in ECE focusing on computer engineering. I also currently work full-time as a Data Scientist while pursuing my Masters. My research interests include: Machine Learning, Graphs and GANs.

2. What is your impression about MCL and USC?

All the members I’ve met in MCL are very knowledgeable in several topics. I am definitely learning a lot by interacting with everyone.  Additionally, everyone I’ve worked with in MCL has been great/enjoyable to work with. I want to thank Professor Kuo for giving me a chance to join the MCL lab and I don’t think my USC experience would be the same without MCL. USC has been great so far, the campus is really nice and it’s easy to make friends even outside of my major.

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

My current goal is to successfully complete the project my team is currently working on regarding model compression. Overall I want to keep learning through research work, publish papers and make connections with my peers in the lab. 

We are so glad to welcome our new MCL member, Yuhang Xu! Here is a short interview with Yuhang:

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

My name is Yuhang Xu. I am a graduate student at USC pursuing a MS degree in Electrical Engineering. My research interests include machine learning and image processing. Recently, I am working on a neural network compression project under the supervision of Prof. Kuo. In my free time I enjoy reading news from around the world, listening to country music, and cooking Chinese food.

2. What is your impression about MCL and USC?

MCL is a mature research group with more than 20 passionate and hardworking individuals. It is prolific and well-organized under the supervision of Prof. Kuo. Prof. Kuo is filled with knowledge and is an inspiration to his students. USC is the perfect balance of academic and social opportunities. During my time at USC, I make friends with people from different cultures.

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

My short-term goal is to complete the current project. It is an interesting one and it has special meaning for me since it is my first project in MCL. I also hope to create strong connections with people in the lab.

Let us hear what she wants to say about her defense and an abstract of her thesis.

Deep learning techniques utilize networks with multiple layers cascaded to map the inputs to desired outputs. To map the entire inputs to desired outputs, useful information should be extracted through the layers. During the mapping, feature extraction and prediction are jointly performed. We do not have direct control for feature extraction. Consequently, some useful information, especially local information, is also discarded in the process.

In this thesis, we specifically study local-aware deep learning techniques from four different aspects: 1) Local-aware network architecture 2) Local-aware proposal generation 3) Local-aware region analysis 4) Local-aware supervision

Specifically, we design a multi-modal attention mechanism for generative visual dialogue system, which simultaneously attends to multi-modal inputs and utilizes extracted local information to generate dialogue responses. We propose a proposal network for fast face detection system for mobile devices, which detects salient facial parts and uses them as local cues for detection of entire faces. We extract representative fashion features by analyzing local regions, which contain local fashion details of humans’ interests. We develop a fashion outfit compatibility learning method, which models each outfit as a graph and learns outfit compatibility using both global and local supervisions on the graphs.

I would like to thank Prof. Kuo and all the lab members for their help. I have learned a lot through my PhD journey and I want to share some feelings and experiences. One essential part of the doctoral training is mental training, from which I have become more persistent, self-disciplined and motivated. As this journey may take several years, maintaining a balanced life is very important. I wish the best to all the lab members and [...]

In the task of fashion compatibility prediction, the goal is to pick an item from a candidate list to complement a partial outfit in the most appealing manner. Existing fashion compatibility recommendation work comprehends clothing images in a single metric space and lacks detailed understanding of users’ preferences in different contexts. To address this problem, we propose a novel Metric-Aware Explainable Graph Network (MAEG). In MAEG, we leverage a Latent Semantic Extraction Network (LSEN) to obtain representations of items in the metric-aware latent semantic space. Then, we develop a graph filtering network and Pairwise Preference Attention (PPA) module to model the interactions between users’ preferences and contextual information. With MAEG, we can provide recommendation to users as well as explain how each item and factor contribute to the final prediction. Extensive experiments on two large-scale real-world datasets reveal that MAEG not only outperforms the state-of-the-art methods, but also provides interpretable insights by highlighting the role of semantic attributes and contextual relationships among items.

MCL Director, Professor Kuo, gave an invited speech at Kyoto University on December 19, 2019. The title of his speech was “From Feedforward-Designed Convolutional Neural Networks (FF-CNNs) to Successive Subspace Learning (SSL)”.  Professor Kuo’s visit to Kyoto University was hosted by Professor Tatsuya Kawahara. The lecture was also an event of IEEE SPS Kansai Chapter.

The abstract of his speech is given below. “Given a convolutional neural network (CNN) architecture, its network parameters are typically determined by backpropagation (BP). The underlying mechanism remains to be a black-box after a large amount of theoretical investigation. In this talk, I will first describe a new interpretable feedforward (FF) design with the LeNet-5 as an example. The FF-designed CNN is a data-centric approach that derives network parameters based on training data statistics layer by layer in a one-pass feedforward manner. To build the convolutional layers, we develop a new signal transform, called the Saab (Subspace approximation with adjusted bias) transform. The bias in filter weights is chosen to annihilate nonlinearity of the activation function. To build the fully-connected (FC) layers, we adopt a label-guided linear least squared regression (LSR) method. To generalize the FF design idea furthermore, we present the notion of “successive subspace learning (SSL)” and present a couple of concrete methods for image and point cloud classification. Experimental results are given to demonstrate the competitive performance of the SSL-based systems. Similarities and differences between SSL and deep learning (DL) are compared.”