Search Results for author:
Found 35 papers, 15 papers with code
Is Your AI Truly Yours? Leveraging Blockchain for Copyrights, Provenance, and Lineage
As Artificial Intelligence (AI) integrates into diverse areas, particularly in content generation, ensuring rightful ownership and ethical use becomes paramount.
From Beginner to Expert: Modeling Medical Knowledge into General LLMs
(3) Specifically for multi-choice questions in the medical domain, we propose a novel Verification-of-Choice approach for prompting engineering, which significantly enhances the reasoning ability of LLMs.
Cryptocurrency in the Aftermath: Unveiling the Impact of the SVB Collapse
We conduct a multi-dimensional investigation, which includes a factual summary, analysis of user sentiment, and examination of market performance.
UMDFood: Vision-language models boost food composition compilation
Nutrition information is crucial in precision nutrition and the food industry.
DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices
Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain gap between a labeled source dataset and an unlabelled target dataset for regression problems.
Blockchain-Empowered Trustworthy Data Sharing: Fundamentals, Applications, and Challenges
Second, we focus on the convergence of blockchain and data sharing to give a clear picture of this landscape and propose a reference architecture for blockchain-based data sharing.
IronForge: An Open, Secure, Fair, Decentralized Federated Learning
Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner.
DARE-GRAM: Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices
Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain gap between a labeled source dataset and an unlabelled target dataset for regression problems.
Continuous Pseudo-Label Rectified Domain Adaptive Semantic Segmentation With Implicit Neural Representations
Unsupervised domain adaptation (UDA) for semantic segmentation aims at improving the model performance on the unlabeled target domain by leveraging a labeled source domain.
One-Shot Domain Adaptive and Generalizable Semantic Segmentation with Class-Aware Cross-Domain Transformers
Thus, we aim to relieve this need on a large number of real data, and explore the one-shot unsupervised sim-to-real domain adaptation (OSUDA) and generalization (OSDG) problem, where only one real-world data sample is available.
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution
We explicitly design an implicit attention network to learn the ensemble weights for the nearby local features.
Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising
To this end, we propose a new multi-scale refined optical flow-guided video denoising method, which is more robust to different noise levels.
Ranked #1 on
Video Denoising
on VideoLQ
Towards Interpretable Video Super-Resolution via Alternating Optimization
These issues can be alleviated by a cascade of three separate sub-tasks, including video deblurring, frame interpolation, and super-resolution, which, however, would fail to capture the spatial and temporal correlations among video sequences.
Multi-agent Actor-Critic with Time Dynamical Opponent Model
In this paper, we propose to exploit the fact that the agents seek to improve their expected cumulative reward and introduce a novel \textit{Time Dynamical Opponent Model} (TDOM) to encode the knowledge that the opponent policies tend to improve over time.
Continual Test-Time Domain Adaptation
However, real-world machine perception systems are running in non-stationary and continually changing environments where the target domain distribution can change over time.
Revisiting Deep Semi-supervised Learning: An Empirical Distribution Alignment Framework and Its Generalization Bound
Benefited from this new perspective, we first propose a new deep semi-supervised learning framework called Semi-supervised Learning by Empirical Distribution Alignment (SLEDA), in which existing technologies from the domain adaptation community can be readily used to address the semi-supervised learning problem through reducing the empirical distribution distance between labeled and unlabeled data.
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a highly labor-intensive process.
Colorectal Polyp Classification from White-light Colonoscopy Images via Domain Alignment
Thus, we propose a novel framework based on a teacher-student architecture for the accurate colorectal polyp classification (CPC) through directly using white-light (WL) colonoscopy images in the examination.
Adaptive Residue-wise Profile Fusion for Low Homologous Protein SecondaryStructure Prediction Using External Knowledge
Protein secondary structure prediction (PSSP) is essential for protein function analysis.
Shallow Feature Matters for Weakly Supervised Object Localization
In practice, our SPOL model first generates the CAMs through a novel element-wise multiplication of shallow and deep feature maps, which filters the background noise and generates sharper boundaries robustly.
Integrating Expert Knowledge with Domain Adaptation for Unsupervised Fault Diagnosis
In this paper, we aim to overcome this limitation by integrating expert knowledge with domain adaptation in a synthetic-to-real framework for unsupervised fault diagnosis.
Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
However, such a supervision is not always available.
Ranked #15 on
Domain Adaptation
on SYNTHIA-to-Cityscapes
(using extra training data)
Both qubits of the singlet state can be steered simultaneously by multiple independent observers via sequential measurement
Quantum correlation is a fundamental property which distinguishes quantum systems from classical ones, and it is also a fragile resource under projective measurement.
Quantum Physics
A Weak Consensus Algorithm and Its Application to High-Performance Blockchain
We apply this consensus algorithm to construct a high-performance blockchain system, called \textit{Sphinx}.
Distributed, Parallel, and Cluster Computing
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search
In this paper, we introduce a new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) architecture search.
Ranked #13 on
Image Generation
on STL-10
Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications
Deep learning applications have been thriving over the last decade in many different domains, including computer vision and natural language understanding.
Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis
We demonstrate in this paper that the performance of domain adversarial methods can be vulnerable to an incomplete target label space during training.
Nanoscale Microscopy Images Colorization Using Neural Networks
Microscopy images are powerful tools and widely used in the majority of research areas, such as biology, chemistry, physics and materials fields by various microscopies (scanning electron microscope (SEM), atomic force microscope (AFM) and the optical microscope, et al.).
Scale- and Context-Aware Convolutional Non-intrusive Load Monitoring
Non-intrusive load monitoring addresses the challenging task of decomposing the aggregate signal of a household's electricity consumption into appliance-level data without installing dedicated meters.
Semi-Supervised Learning by Augmented Distribution Alignment
We reveal that an essential sampling bias exists in semi-supervised learning due to the limited number of labeled samples, which often leads to a considerable empirical distribution mismatch between labeled data and unlabeled data.
Domain Adaptive Transfer Learning for Fault Diagnosis
Thanks to digitization of industrial assets in fleets, the ambitious goal of transferring fault diagnosis models fromone machine to the other has raised great interest.
Convolutional Sequence to Sequence Non-intrusive Load Monitoring
A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this paper.
Short-term Load Forecasting with Deep Residual Networks
We present in this paper a model for forecasting short-term power loads based on deep residual networks.
Substation Signal Matching with a Bagged Token Classifier
Currently, engineers at substation service providers match customer data with the corresponding internally used signal names manually.
ContextVP: Fully Context-Aware Video Prediction
Video prediction models based on convolutional networks, recurrent networks, and their combinations often result in blurry predictions.
