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Found 10 papers, 2 papers with code
Task-Aware Quantization Network for JPEG Image Compression
We propose to learn a deep neural network for JPEG image compression, which predicts image-specific optimized quantization tables fully compatible with the standard JPEG encoder and decoder.
PECI-Net: Bolus segmentation from video fluoroscopic swallowing study images using preprocessing ensemble and cascaded inference
Bolus segmentation is crucial for the automated detection of swallowing disorders in videofluoroscopic swallowing studies (VFSS).
A Training-Free Defense Framework for Robust Learned Image Compression
We study the robustness of learned image compression models against adversarial attacks and present a training-free defense technique based on simple image transform functions.
Observation-Guided Diffusion Probabilistic Models
We propose a novel diffusion-based image generation method called the observation-guided diffusion probabilistic model (OGDM), which effectively addresses the tradeoff between quality control and fast sampling.
Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform
In addition, the proposed framework allows us to perform task-aware image compressions for various tasks, e. g., classification, by efficiently estimating optimized quality maps specific to target tasks for our encoding network.
MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators
We propose a framework of generative adversarial networks with multiple discriminators, which collaborate to represent a real dataset more effectively.
Risk-Conditioned Distributional Soft Actor-Critic for Risk-Sensitive Navigation
Modern navigation algorithms based on deep reinforcement learning (RL) show promising efficiency and robustness.
Multi-focus Attention Network for Efficient Deep Reinforcement Learning
In multi-agent cooperative task experiments, our model shows 20% faster learning than existing state-of-the-art model.
Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals
To solve this issue, the subgoal and option framework have been proposed.
Human Body Orientation Estimation using Convolutional Neural Network
However, in most of the service robot applications, the user needs to move himself/herself to allow the robot to see him/her face to face.
