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Found 12 papers, 1 papers with code
Efficient Transfer Learning in Diffusion Models via Adversarial Noise
Diffusion Probabilistic Models (DPMs) have demonstrated substantial promise in image generation tasks but heavily rely on the availability of large amounts of training data.
Boosting Diffusion Models with an Adaptive Momentum Sampler
Our proposed sampler can be readily applied to a pre-trained diffusion model, utilizing momentum mechanisms and adaptive updating to smooth the reverse sampling process and ensure stable generation, resulting in outputs of enhanced quality.
GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts
In the context of the rapid development of large language models, we have meticulously trained and introduced the GujiBERT and GujiGPT language models, which are foundational models specifically designed for intelligent information processing of ancient texts.
Learning Structure-Guided Diffusion Model for 2D Human Pose Estimation
One of the mainstream schemes for 2D human pose estimation (HPE) is learning keypoints heatmaps by a neural network.
Confidence Attention and Generalization Enhanced Distillation for Continuous Video Domain Adaptation
The second module compares the outputs of augmented data from the current model to the outputs of weakly augmented data from the source model, forming a novel consistency regularization on the model to alleviate the accumulation of prediction errors.
Ambient FSK Backscatter Communications using LTE Cell Specific Reference Signals
Long Term Evolution (LTE) signal is ubiquitously present in electromagnetic (EM) background environment, which make it an attractive signal source for the ambient backscatter communications (AmBC).
Ambient backscatter communications using LTE cell specific reference signals
Long Term Evolution (LTE) systems provide ubiquitous coverage for mobile communications, which makes it a promising candidate to be used as a signal source in the ambient backscatter communications.
Calibrating Class Weights with Multi-Modal Information for Partial Video Domain Adaptation
It utilizes a novel class weight calibration method to alleviate the negative transfer caused by incorrect class weights.
Domain Adaptation via Bidirectional Cross-Attention Transformer
Specifically, in BCAT, we design a weight-sharing quadruple-branch transformer with a bidirectional cross-attention mechanism to learn domain-invariant feature representations.
Optimum Multi-Antenna Ambient Backscatter Receiver for General Binary-Modulated Signal
Efforts have been put into backscatter signal detection as the detection performance is limited by the low signal-to-interference-plus-noise ratio (SINR) of the signal at the receiver.
Coherent Multi-antenna Receiver for BPSK-modulated Ambient Backscatter Tags
The performance of the proposed receiver is compared with the ideal coherent receiver that has a perfect phase information, and also with the performance of non-coherent receiver, which assumes distributions for ambient signal and phase offset caused by excess length of the backscatter path.
An Unsupervised Deep Learning Approach for Scenario Forecasts
Simulation results indicate our method is able to generate scenarios that capture spatial and temporal correlations.
Optimization and Control
