Object detection and Classification on an embedded GPU or FPGA system
The DAC System Design Contest focuses on object detection and classification on an embedded GPU or FPGA system. Contestants will receive a training dataset provided by Baidu, and a hidden dataset will be used to evaluate the performance of the designs in terms of accuracy and speed.
Jul 14, 2023
Efficient Unbiased Training of Large-scale Distributed WGAN
This project considers a multi-GPU settings where multiple processors/workers/clients have access to local stochastic dual vectors. This setting includes a broad range of important problems from distributed convex minimization to min-max and games. Using quantized generalized extra-gradient (Q-GenX) the given monotone variational inequality problem (WGAN) can be solved.
Mar 1, 2023
Learning Object-Centric Representations of Visually Complex Scenes
Learning distributed vector representations has been a driving force behind the remarkable success of deep neural networks in perceptual tasks on ImageNet-like photos with solitary objects (cars, trunks, etc.) in the center. One emerging paradigm for capturing the compositional nature of complex scenes is to learn slot-based object-centric representations. Nevertheless, existing methods to date are still largely limited to simple synthetic settings with low visual complexity.
Dec 1, 2022