21 research outputs found
Evolving parametrized Loss for Image Classification Learning on Small Datasets
This paper proposes a meta-learning approach to evolving a parametrized loss
function, which is called Meta-Loss Network (MLN), for training the image
classification learning on small datasets. In our approach, the MLN is embedded
in the framework of classification learning as a differentiable objective
function. The MLN is evolved with the Evolutionary Strategy algorithm (ES) to
an optimized loss function, such that a classifier, which optimized to minimize
this loss, will achieve a good generalization effect. A classifier learns on a
small training dataset to minimize MLN with Stochastic Gradient Descent (SGD),
and then the MLN is evolved with the precision of the small-dataset-updated
classifier on a large validation dataset. In order to evaluate our approach,
the MLN is trained with a large number of small sample learning tasks sampled
from FashionMNIST and tested on validation tasks sampled from FashionMNIST and
CIFAR10. Experiment results demonstrate that the MLN effectively improved
generalization compared to classical cross-entropy error and mean squared
error
A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms
Mammography remains the most prevalent imaging tool for early breast cancer
screening. The language used to describe abnormalities in mammographic reports
is based on the breast Imaging Reporting and Data System (BI-RADS). Assigning a
correct BI-RADS category to each examined mammogram is a strenuous and
challenging task for even experts. This paper proposes a new and effective
computer-aided diagnosis (CAD) system to classify mammographic masses into four
assessment categories in BI-RADS. The mass regions are first enhanced by means
of histogram equalization and then semiautomatically segmented based on the
region growing technique. A total of 130 handcrafted BI-RADS features are then
extrcated from the shape, margin, and density of each mass, together with the
mass size and the patient's age, as mentioned in BI-RADS mammography. Then, a
modified feature selection method based on the genetic algorithm (GA) is
proposed to select the most clinically significant BI-RADS features. Finally, a
back-propagation neural network (BPN) is employed for classification, and its
accuracy is used as the fitness in GA. A set of 500 mammogram images from the
digital database of screening mammography (DDSM) is used for evaluation. Our
system achieves classification accuracy, positive predictive value, negative
predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%,
and 79.3%, respectively. To our best knowledge, this is the best current result
for BI-RADS classification of breast masses in mammography, which makes the
proposed system promising to support radiologists for deciding proper patient
management based on the automatically assigned BI-RADS categories
L2T-DLN: Learning to Teach with Dynamic Loss Network
With the concept of teaching being introduced to the machine learning
community, a teacher model start using dynamic loss functions to teach the
training of a student model. The dynamic intends to set adaptive loss functions
to different phases of student model learning. In existing works, the teacher
model 1) merely determines the loss function based on the present states of the
student model, i.e., disregards the experience of the teacher; 2) only utilizes
the states of the student model, e.g., training iteration number and
loss/accuracy from training/validation sets, while ignoring the states of the
loss function. In this paper, we first formulate the loss adjustment as a
temporal task by designing a teacher model with memory units, and, therefore,
enables the student learning to be guided by the experience of the teacher
model. Then, with a dynamic loss network, we can additionally use the states of
the loss to assist the teacher learning in enhancing the interactions between
the teacher and the student model. Extensive experiments demonstrate our
approach can enhance student learning and improve the performance of various
deep models on real-world tasks, including classification, objective detection,
and semantic segmentation scenarios
LCCo: Lending CLIP to Co-Segmentation
This paper studies co-segmenting the common semantic object in a set of
images. Existing works either rely on carefully engineered networks to mine the
implicit semantic information in visual features or require extra data (i.e.,
classification labels) for training. In this paper, we leverage the contrastive
language-image pre-training framework (CLIP) for the task. With a backbone
segmentation network that independently processes each image from the set, we
introduce semantics from CLIP into the backbone features, refining them in a
coarse-to-fine manner with three key modules: i) an image set feature
correspondence module, encoding global consistent semantic information of the
image set; ii) a CLIP interaction module, using CLIP-mined common semantics of
the image set to refine the backbone feature; iii) a CLIP regularization
module, drawing CLIP towards this co-segmentation task, identifying the best
CLIP semantic and using it to regularize the backbone feature. Experiments on
four standard co-segmentation benchmark datasets show that the performance of
our method outperforms state-of-the-art methods
Improving Shape Retrieval by Integrating AIR and Modified Mutual k
In computer vision, image retrieval remained a significant problem and recent resurgent of image retrieval also relies on other postprocessing methods to improve the accuracy instead of solely relying on good feature representation. Our method addressed the shape retrieval of binary images. This paper proposes a new integration scheme to best utilize feature representation along with contextual information. For feature representation we used articulation invariant representation; dynamic programming is then utilized for better shape matching followed by manifold learning based postprocessing modified mutual kNN graph to further improve the similarity score. We conducted extensive experiments on widely used MPEG-7 database of shape images by so-called bulls-eye score with and without normalization of modified mutual kNN graph which clearly indicates the importance of normalization. Finally, our method demonstrated better results compared to other methods. We also computed the computational time with another graph transduction method which clearly shows that our method is computationally very fast. Furthermore, to show consistency of postprocessing method, we also performed experiments on challenging ORL and YALE face datasets and improved baseline results