2701 results for "uber vip" across all locations

M. Ren, R. Liao, E. Fetaya, R. Zemel
This paper addresses this problem, incremental few- shot learning, where a regular classification network has already been trained to recognize a set of base classes, and several extra novel classes are being considered, each with only a few labeled examples. After learning the novel classes, the model is then evaluated on the overall classification performance on both base and novel classes. To this end, we propose a meta-learning model, the Attention Attractor Networks, which regularizes the learning of novel classes. [PDF]
Conference on Neural Information Processing Systems (NeurIPS), 2019

L. Gueguen, A. Sergeev, B. Kadlec, R. Liu, J. Yosinski
The simple, elegant approach of training convolutional neural networks (CNNs) directly from RGB pixels has enjoyed overwhelming empirical success. But can more performance be squeezed out of networks by using different input representations? In this paper we propose and explore a simple idea: train CNNs directly on the blockwise discrete cosine transform (DCT) coefficients computed and available in the middle of the JPEG codec. […] [PDF]
Advances in Neural Information Processing Systems (NeurIPS), 2018