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Found 23 papers, 12 papers with code
Improving Deep Embedded Clustering via Learning Cluster-level Representations
In this paper, we propose a novel DEC model, which we named the deep embedded clustering model with cluster-level representation learning (DECCRL) to jointly learn cluster and instance level representations.
THQA: A Perceptual Quality Assessment Database for Talking Heads
In the realm of media technology, digital humans have gained prominence due to rapid advancements in computer technology.
Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition
The past years have witnessed a proliferation of large language models (LLMs).
LightSleepNet: Design of a Personalized Portable Sleep Staging System Based on Single-Channel EEG
This paper proposed LightSleepNet - a light-weight, 1-d Convolutional Neural Network (CNN) based personalized architecture for real-time sleep staging, which can be implemented on various mobile platforms with limited hardware resources.
Multi-Channel Multi-Domain based Knowledge Distillation Algorithm for Sleep Staging with Single-Channel EEG
The proposed algorithm achieves a state-of-the-art single-channel sleep staging accuracy of 86. 5%, with only 0. 6% deterioration from the state-of-the-art multi-channel model.
RetroOOD: Understanding Out-of-Distribution Generalization in Retrosynthesis Prediction
Machine learning-assisted retrosynthesis prediction models have been gaining widespread adoption, though their performances oftentimes degrade significantly when deployed in real-world applications embracing out-of-distribution (OOD) molecules or reactions.
Learning a Deep Color Difference Metric for Photographic Images
Most well-established and widely used color difference (CD) metrics are handcrafted and subject-calibrated against uniformly colored patches, which do not generalize well to photographic images characterized by natural scene complexities.
ConfounderGAN: Protecting Image Data Privacy with Causal Confounder
The success of deep learning is partly attributed to the availability of massive data downloaded freely from the Internet.
Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation
The advent of the big data era brought new opportunities and challenges to draw treatment effect in data fusion, that is, a mixed dataset collected from multiple sources (each source with an independent treatment assignment mechanism).
Measuring Perceptual Color Differences of Smartphone Photographs
Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography.
Minimizing Memorization in Meta-learning: A Causal Perspective
Meta-learning has emerged as a potent paradigm for quick learning of few-shot tasks, by leveraging the meta-knowledge learned from meta-training tasks.
Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling
We study the problem of efficient semantic segmentation of large-scale 3D point clouds.
Semi-Supervised Deep Ensembles for Blind Image Quality Assessment
Ensemble methods are generally regarded to be better than a single model if the base learners are deemed to be "accurate" and "diverse."
Troubleshooting Blind Image Quality Models in the Wild
Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics.
A CNN-Based Blind Denoising Method for Endoscopic Images
The number of iterations is reduced about 36% by using transfer learning in our DIP process.
Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment
We then seek pairs of images by comparing the baseline model with a set of full-reference IQA methods in gMAD.
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
We study the problem of efficient semantic segmentation for large-scale 3D point clouds.
Ranked #3 on
Semantic Segmentation
on Toronto-3D L002
Learning with Stochastic Guidance for Navigation
Due to the sparse rewards and high degree of environment variation, reinforcement learning approaches such as Deep Deterministic Policy Gradient (DDPG) are plagued by issues of high variance when applied in complex real world environments.
Robotics
Neural Allocentric Intuitive Physics Prediction from Real Videos
In this framework, real images are first converted to a synthetic domain representation that reduces complexity arising from lighting and texture.
3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations
This is further confounded by the fact that shape information about encountered objects in the real world is often impaired by occlusions, noise and missing regions e. g. a robot manipulating an object will only be able to observe a partial view of the entire solid.
Defo-Net: Learning Body Deformation using Generative Adversarial Networks
Modelling the physical properties of everyday objects is a fundamental prerequisite for autonomous robots.
Robotics
2D Reconstruction of Small Intestine's Interior Wall
The proposed new wireless endoscopic image stitching method consists of two main steps to improve the accuracy and efficiency of image registration.
A Bio-inspired Collision Detecotr for Small Quadcopter
The observed results from the experiments demonstrated that the LGMD collision detector is feasible to work as a vision module for the quadcopter's collision avoidance task.
