4 research outputs found
Adversarial Consistent Learning on Partial Domain Adaptation of PlantCLEF 2020 Challenge
Domain adaptation is one of the most crucial techniques to mitigate the
domain shift problem, which exists when transferring knowledge from an abundant
labeled sourced domain to a target domain with few or no labels. Partial domain
adaptation addresses the scenario when target categories are only a subset of
source categories. In this paper, to enable the efficient representation of
cross-domain plant images, we first extract deep features from pre-trained
models and then develop adversarial consistent learning () in a unified
deep architecture for partial domain adaptation. It consists of source domain
classification loss, adversarial learning loss, and feature consistency loss.
Adversarial learning loss can maintain domain-invariant features between the
source and target domains. Moreover, feature consistency loss can preserve the
fine-grained feature transition between two domains. We also find the shared
categories of two domains via down-weighting the irrelevant categories in the
source domain. Experimental results demonstrate that training features from
NASNetLarge model with proposed architecture yields promising results on
the PlantCLEF 2020 Challenge
Adversarial Regression Learning for Bone Age Estimation
Estimation of bone age from hand radiographs is essential to determine
skeletal age in diagnosing endocrine disorders and depicting the growth status
of children. However, existing automatic methods only apply their models to
test images without considering the discrepancy between training samples and
test samples, which will lead to a lower generalization ability. In this paper,
we propose an adversarial regression learning network (ARLNet) for bone age
estimation. Specifically, we first extract bone features from a fine-tuned
Inception V3 neural network and propose regression percentage loss for
training. To reduce the discrepancy between training and test data, we then
propose adversarial regression loss and feature reconstruction loss to
guarantee the transition from training data to test data and vice versa,
preserving invariant features from both training and test data. Experimental
results show that the proposed model outperforms state-of-the-art methods.Comment: 27th Information Processing in Medical Imaging (IPMI
Impact of ImageNet Model Selection on Domain Adaptation
Deep neural networks are widely used in image classification problems.
However, little work addresses how features from different deep neural networks
affect the domain adaptation problem. Existing methods often extract deep
features from one ImageNet model, without exploring other neural networks. In
this paper, we investigate how different ImageNet models affect transfer
accuracy on domain adaptation problems. We extract features from sixteen
distinct pre-trained ImageNet models and examine the performance of twelve
benchmarking methods when using the features. Extensive experimental results
show that a higher accuracy ImageNet model produces better features, and leads
to higher accuracy on domain adaptation problems (with a correlation
coefficient of up to 0.95). We also examine the architecture of each neural
network to find the best layer for feature extraction. Together, performance
from our features exceeds that of the state-of-the-art in three benchmark
datasets
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". Today, more than 40 years after the reform and opening, people
have basically solved the problem of food and clothing, and the urgent problem
is housing. Nowadays, due to the storm of long-term rental apartment
intermediary platforms such as eggshell, increasing the sense of insecurity of
renters, as well as the urbanization in recent years and the scramble for
people in major cities, this will make the future real estate market
competition more intense. In order to better grasp the real estate price, let
consumers buy a house reasonably, and provide a reference for the government to
formulate policies, this paper summarizes the existing methods of house price
prediction and proposes a house price prediction method based on mixed depth
vision and text features.Comment: in Chinese languag