AI Innovation Institute

This workshop synthesizes the latest research on the impact of AI usage in education so that you could make informed decisions on whether and how to use AI to facilitate your learning. You might have seen conflicting reports on whether the use of AI is good for learning. In this workshop, we are going to tease out, drawing on the latest research, which types of AI usage are beneficial or harmful for different kinds of learning. At the end of the workshop, you should walk away with more clarity on when and how to use AI for your own learning. Join PRODIG+ fellow on critical AI, Zheng Fu, in this informative workshop.

Register for this Zoom workshop.

Deep Surface MeshesPascal FuaEPFLGeometric Deep Learning has recently made striking progress with the advent of Deep Implicit Fields (SDFs). They allow for detailed modeling of watertight surfaces of arbitrary topology while not relying on a 3D Euclidean grid, resulting in a learnable 3D surface parameterization that is not limited in resolution. Unfortunately, they have not yet reached their full potential for applications that require an explicit surface representation in terms of vertices and facets because converting the SDF to such a 3D mesh representation requires a marching-cube algorithm, whose output cannot be easily differentiated with respect to the SDF parameters. In this talk, I will discuss our approach to overcoming this limitation and implementing convolutional neural nets that output complex 3D surface meshes while remaining fully-differentiable and end-to-end trainable. I will also present applications to single view reconstruction, physically-driven Shape optimization, and bio-medical image segmentation.

Pascal Fua received an engineering degree from Ecole Polytechnique, Paris, in 1984 and a Ph.D. in Computer Science from the University of Orsay in 1989. He joined EPFL (Swiss Federal Institute of Technology) in 1996 where he is a Professor in the School of Computer and Communication Science and head of the Computer Vision Lab. Before that, he worked at SRI International and at INRIA Sophia-Antipolis as a Computer Scientist. His research interests include shape modeling and motion recovery from images, analysis of microscopy images, and Augmented Reality. He has (co)authored over 300 publications in refereed journals and conferences. He has received several ERC grants. He is an IEEE Fellow and has been an Associate Editor of IEEE journal Transactions for Pattern Analysis and Machine Intelligence. He often serves as program committee member, area chair, and program chair of major vision conferences and has cofounded three spinoff companies. 

Over the past decade, larger datasets, hardware accelerations, and network architecture improvements have contributed to phenomenal achievements in many tasks of computer

vision. However, in the absence of large datasets, computer vision models struggle to learn

general representations which results in poor performance. Few-shot learning tries to address 

this problem by proposing models which learn from a few examples.

I first give an overall review of few-shot learning methods. I particularly focus on generative Few Shot Learning(FSL) methods, which augment the scarce categories in a dataset by generating samples for those rare categories. As the actual class distribution can be complex and lie very close to each other, the sample generated for one class can be noisy or lie close to another class.  However, none of the current FS generative methods perform any form of quality control of the generated samples.

In this work, I propose to identify and remove the generated samples that are less likely to be in the distribution of the few-shot class. Here I particularly deal with few-shot scenarios where the

prior information of the relationship between the classes based on visual  similarity is available. The main idea is to exploit these priors to better identify the unreliable generated samples.

Particularly, I have proposed two methods based on class relationship to detect noisy generated samples. In the first method, we assume that the embedding space of each class follows a Gaussian distribution.  From this assumption, I propose Gaussian Neighborhood (GN), a method to estimate how likely a generated sample is drawn from the estimated distribution of a few-shot class.  We evaluate this method on the Hematopoiesis dataset.  By simply eliminating samples based on thresholding our proposed GN scores, the few-shot  classification  performance  is  improved by 5% and 2% in five shot and one shot respectively, compared to the model trained on all generated images. 

The GN scores represent the similarity distances from the generated samples to their classes, based on the assumption that each class is a Gaussian distribution.  However, this assumption might be strict in many scenarios since the real distributions of data can be arbitrarily complex.  Thus in my second proposed method, I aim to learn such similarity distances directly from data via metric learning. I propose to train a deep-network to regress the similarity distance between a pair of samples.  This network is trained using both the class-level  visual  similarity  information  and  the  class  labels.   This method improves the 1-shot and 5-shot classification performances by 0.5% and 1% respectively, compared to GN.