The original ImageNet dataset is a popular large-scale benchmark for training Deep Neural Networks.
The proposed model then warps the input frames, depth maps, and contextual features based on the optical flow and local interpolation kernels for synthesizing the output frame.
The resulting model significantly outperforms state-of-the-art models with similar accuracy in terms of mCE and inference throughput.
In recent years, neural networks have been extensively deployed for computer vision tasks, particularly visual classification problems, where new algorithms reported to achieve or even surpass the human performance.
Pre-trained language models have been dominating the field of natural language processing in recent years, and have led to significant performance gains for various complex natural language tasks.
SOTA for Sentiment Analysis on DBRD
We detail a new framework for privacy preserving deep learning and discuss its assets.