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Found 9 papers, 2 papers with code
Keypoint Detection Empowered Near-Field User Localization and Channel Reconstruction
We visualize the sparse channel matrix in the transformed domain as a channel image and design the channel keypoint detection network (CKNet) to locate the user and scatterers in high speed.
Mask Conditional Synthetic Satellite Imagery
In this paper we propose a mask-conditional synthetic image generation model for creating synthetic satellite imagery datasets.
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.
Associative Memory Based Experience Replay for Deep Reinforcement Learning
AMPER replaces the widely-used time-costly tree-traversal-based priority sampling in PER while preserving the learning performance.
Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation
Although the U-Net architecture has been extensively used for segmentation of medical images, we address two of its shortcomings in this work.
Towards Non-task-specific Distillation of BERT via Sentence Representation Approximation
There are plenty of studies showing that the knowledge distillation is efficient in transferring the knowledge from BERT into the model with a smaller size of parameters.
Guiding Variational Response Generator to Exploit Persona
Leveraging persona information of users in Neural Response Generators (NRG) to perform personalized conversations has been considered as an attractive and important topic in the research of conversational agents over the past few years.
MemeFaceGenerator: Adversarial Synthesis of Chinese Meme-face from Natural Sentences
Chinese meme-face is a special kind of internet subculture widely spread in Chinese Social Community Networks.
Why Do Neural Response Generation Models Prefer Universal Replies?
Recent advances in sequence-to-sequence learning reveal a purely data-driven approach to the response generation task.
