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Deep Learning, Computer Vision and CUDA


Term:

IAP and Spring

Department:

6: Electrical Engineering and Computer Science

Faculty Supervisor:

Tomaso Poggio

Faculty email:

tp@ai.mit.edu

Apply by:

1/3/2020

Contact:

LQL@mit.edu

Project Description

Project 1: Although humans intuitively understand/interpret the world in terms of discrete objects. State-of-the-art machine vision systems do not have a good representation of visual objects. In a series of recent work, we try to incorporate the knowledge of object into deep learning networks and proposed a class of models we call "object-oriented" deep networks (OONets). https://cbmm.mit.edu/publications/object-oriented-deep-learning https://dspace.mit.edu/handle/1721.1/113002 Students are encouraged to either: 1. Try variants of our model (already implemented in PyTorch) on a wide range of computer vision tasks. (GAN, object segmentation, etc.) 2. Implement our models with Tensorflow. 3. Accelerate our models with NVIDIA CUDA (we have basic implementations in CUDA but we can further speed them up). Project 2: Study state-of-the-art object detection algorithms. Apply our OONets in Project 1 to detection tasks. Project 3: My other ongoing projects include biologically-plausible learning without backpropagation, content and style separation with GANs and developing a deep learning framework on Matlab. If you have your own ideas, we can also discuss.

Pre-requisites

Good at Python or Matlab