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Found 25 papers, 9 papers with code
DPATD: Dual-Phase Audio Transformer for Denoising
Recent high-performance transformer-based speech enhancement models demonstrate that time domain methods could achieve similar performance as time-frequency domain methods.
Deep Feature Registration for Unsupervised Domain Adaptation
While unsupervised domain adaptation has been explored to leverage the knowledge from a labeled source domain to an unlabeled target domain, existing methods focus on the distribution alignment between two domains.
LaksNet: an end-to-end deep learning model for self-driving cars in Udacity simulator
The majority of road accidents occur because of human errors, including distraction, recklessness, and drunken driving.
Complex Image Generation SwinTransformer Network for Audio Denoising
Achieving high-performance audio denoising is still a challenging task in real-world applications.
Stall Number Detection of Cow Teats Key Frames
In this paper, we present a small cow stall number dataset named CowStallNumbers, which is extracted from cow teat videos with the goal of advancing cow stall number detection.
Lung segmentation with NASNet-Large-Decoder Net
Lung cancer has emerged as a severe disease that threatens human life and health.
BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds
Audio denoising has been explored for decades using both traditional and deep learning-based methods.
House Price Prediction Based On Deep Learning
Since ancient times, what Chinese people have been pursuing is very simple, which is nothing more than "to live and work happily, to eat and dress comfortable".
A Survey of Unsupervised Domain Adaptation for Visual Recognition
The principal objective of UDA is to reduce the domain discrepancy between the labeled source data and unlabeled target data and to learn domain-invariant representations across the two domains during training.
Deep Least Squares Alignment for Unsupervised Domain Adaptation
Unsupervised domain adaptation leverages rich information from a labeled source domain to model an unlabeled target domain.
Automatic Head Overcoat Thickness Measure with NASNet-Large-Decoder Net
To further improve segmentation results, we are the first to propose a post-processing layer to remove irrelevant portions in the segmentation result.
Enhanced Separable Disentanglement for Unsupervised Domain Adaptation
The reconstructed features are also not sufficiently used during training.
Correlated Adversarial Joint Discrepancy Adaptation Network
To address these issues, we propose a novel approach called correlated adversarial joint discrepancy adaptation network (CAJNet), which minimizes the joint discrepancy of two domains and achieves competitive performance with tuning parameters using the correlated label.
Deep Spherical Manifold Gaussian Kernel for Unsupervised Domain Adaptation
To align the conditional distributions, we further develop an easy-to-hard pseudo label refinement process to improve the quality of the pseudo labels and then reduce categorical spherical manifold Gaussian kernel geodesic loss.
Efficient Pre-trained Features and Recurrent Pseudo-Labeling in Unsupervised Domain Adaptation
In this paper, we show how to efficiently opt for the best pre-trained features from seventeen well-known ImageNet models in unsupervised DA problems.
Adversarial Regression Learning for Bone Age Estimation
In this paper, we propose an adversarial regression learning network (ARLNet) for bone age estimation.
Adversarial Consistent Learning on Partial Domain Adaptation of PlantCLEF 2020 Challenge
Adversarial learning loss can maintain domain-invariant features between the source and target domains.
Bayesian Geodesic Regression on Riemannian Manifolds
However, it cannot automatically choose the dimensionality of data.
Impact of ImageNet Model Selection on Domain Adaptation
We extract features from sixteen distinct pre-trained ImageNet models and examine the performance of twelve benchmarking methods when using the features.
Mixture Probabilistic Principal Geodesic Analysis
Dimensionality reduction on Riemannian manifolds is challenging due to the complex nonlinear data structures.
Corticospinal Tract (CST) reconstruction based on fiber orientation distributions(FODs) tractography
The Corticospinal Tract (CST) is a part of pyramidal tract (PT), and it can innervate the voluntary movement of skeletal muscle through spinal interneurons (the 4th layer of the Rexed gray board layers), and anterior horn motorneurons (which control trunk and proximal limb muscles).
Modified Distribution Alignment for Domain Adaptation with Pre-trained Inception ResNet
Deep neural networks have been widely used in computer vision.
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A Regressive Convolution Neural network and Support Vector Regression Model for Electricity Consumption Forecasting
Electricity consumption forecasting has important implications for the mineral companies on guiding quarterly work, normal power system operation, and the management.
Automated identification of hookahs (waterpipes) on Instagram: an application in feature extraction using Convolutional Neural Network and Support Vector Machine classification
Objective: This study demonstrates how convolutional neural network (CNN) can be used to extract unique features within an image and how SVM can then be used to classify the image.
Electricity consumption forecasting method based on MPSO-BP neural network model
This paper deals with the problem of the electricity consumption forecasting method.
