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Found 6 papers, 2 papers with code
Training High-Performance and Large-Scale Deep Neural Networks with Full 8-bit Integers
In this way, all the operations in the training and inference can be bit-wise operations, pushing towards faster processing speed, decreased memory cost, and higher energy efficiency.
DashNet: A Hybrid Artificial and Spiking Neural Network for High-speed Object Tracking
To the best of our knowledge, DashNet is the first framework that can integrate and process ANNs and SNNs in a hybrid paradigm, which provides a novel solution to achieve both effectiveness and efficiency for high-speed object tracking.
Transfer Learning in General Lensless Imaging through Scattering Media
In summary, this work provides a new solution for lensless imaging through scattering media using transfer learning in DNNs.
Restoring Negative Information in Few-Shot Object Detection
In this paper, we restore the negative information in few-shot object detection by introducing a new negative- and positive-representative based metric learning framework and a new inference scheme with negative and positive representatives.
Training and Inference for Integer-Based Semantic Segmentation Network
However, neural network quantization can be used to reduce computation load while maintaining comparable accuracy and original network structure.
Towards Efficient Full 8-bit Integer DNN Online Training on Resource-limited Devices without Batch Normalization
In summary, the EOQ framework is specially designed for reducing the high cost of convolution and BN in network training, demonstrating a broad application prospect of online training in resource-limited devices.
