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Found 7 papers, 1 papers with code
KL-Divergence-Based Region Proposal Network for Object Detection
The learning of the region proposal in object detection using the deep neural networks (DNN) is divided into two tasks: binary classification and bounding box regression task.
S3: A Spectral-Spatial Structure Loss for Pan-Sharpening Networks
Our proposed S3 loss can be very effectively utilized for pan-sharpening with various types of CNN structures, resulting in significant visual improvements on PS images with suppressed artifacts.
Genetic-Gated Networks for Deep Reinforcement
We introduce the Genetic-Gated Networks (G2Ns), simple neural networks that combine a gate vector composed of binary genetic genes in the hidden layer(s) of networks.
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones.
Single Image Super-Resolution Using Lightweight CNN with Maxout Units
To the best of our knowledge, we are the first to incorporate MU into SR applications and show promising performance results.
BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning
Our method learns hundreds to thousand times faster than the conventional methods by learning only a handful of core cluster information, which shows that deep RL agents can effectively learn through the shared knowledge from other agents.
Residual Features and Unified Prediction Network for Single Stage Detection
They are much faster than two stage detectors that use region proposal networks (RPN) without much degradation in the detection performances.
