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  1. Article ; Online: Model Compression Based on Differentiable Network Channel Pruning.

    Zheng, Yu-Jie / Chen, Si-Bao / Ding, Chris H Q / Luo, Bin

    IEEE transactions on neural networks and learning systems

    2023  Volume 34, Issue 12, Page(s) 10203–10212

    Abstract: Although neural networks have achieved great success in various fields, applications on mobile devices are limited by the computational and storage costs required for large models. The model compression (neural network pruning) technology can ... ...

    Abstract Although neural networks have achieved great success in various fields, applications on mobile devices are limited by the computational and storage costs required for large models. The model compression (neural network pruning) technology can significantly reduce network parameters and improve computational efficiency. In this article, we propose a differentiable network channel pruning (DNCP) method for model compression. Unlike existing methods that require sampling and evaluation of a large number of substructures, our method can efficiently search for optimal substructure that meets resource constraints (e.g., FLOPs) through gradient descent. Specifically, we assign a learnable probability to each possible number of channels in each layer of the network, relax the selection of a particular number of channels to a softmax over all possible numbers of channels, and optimize the learnable probability in an end-to-end manner through gradient descent. After the network parameters are optimized, we prune the network according to the learnable probability to obtain the optimal substructure. To demonstrate the effectiveness and efficiency of DNCP, experiments are conducted with ResNet and MobileNet V2 on CIFAR, Tiny ImageNet, and ImageNet datasets.
    Language English
    Publishing date 2023-11-30
    Publishing country United States
    Document type Journal Article
    ISSN 2162-2388
    ISSN (online) 2162-2388
    DOI 10.1109/TNNLS.2022.3165123
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning.

    Chen, Yixiong / Zhang, Chunhui / Ding, Chris H Q / Liu, Li

    IEEE transactions on medical imaging

    2023  Volume 42, Issue 5, Page(s) 1388–1400

    Abstract: Well-annotated medical datasets enable deep neural networks (DNNs) to gain strong power in extracting lesion-related features. Building such large and well-designed medical datasets is costly due to the need for high-level expertise. Model pre-training ... ...

    Abstract Well-annotated medical datasets enable deep neural networks (DNNs) to gain strong power in extracting lesion-related features. Building such large and well-designed medical datasets is costly due to the need for high-level expertise. Model pre-training based on ImageNet is a common practice to gain better generalization when the data amount is limited. However, it suffers from the domain gap between natural and medical images. In this work, we pre-train DNNs on ultrasound (US) domains instead of ImageNet to reduce the domain gap in medical US applications. To learn US image representations based on unlabeled US videos, we propose a novel meta-learning-based contrastive learning method, namely Meta Ultrasound Contrastive Learning (Meta-USCL). To tackle the key challenge of obtaining semantically consistent sample pairs for contrastive learning, we present a positive pair generation module along with an automatic sample weighting module based on meta-learning. Experimental results on multiple computer-aided diagnosis (CAD) problems, including pneumonia detection, breast cancer classification, and breast tumor segmentation, show that the proposed self-supervised method reaches state-of-the-art (SOTA). The codes are available at https://github.com/Schuture/Meta-USCL.
    MeSH term(s) Ultrasonography ; Diagnosis, Computer-Assisted ; Neural Networks, Computer
    Language English
    Publishing date 2023-05-02
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 622531-7
    ISSN 1558-254X ; 0278-0062
    ISSN (online) 1558-254X
    ISSN 0278-0062
    DOI 10.1109/TMI.2022.3228254
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2546: Show issues Location:
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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  3. Article ; Online: Revisiting L

    Jiang, Bo / Ding, Chris

    IEEE transactions on neural networks and learning systems

    2020  Volume 31, Issue 12, Page(s) 5624–5629

    Abstract: In many real-world applications, data usually contain outliers. One popular approach is to use the ... ...

    Abstract In many real-world applications, data usually contain outliers. One popular approach is to use the L
    Language English
    Publishing date 2020-11-30
    Publishing country United States
    Document type Journal Article
    ISSN 2162-2388
    ISSN (online) 2162-2388
    DOI 10.1109/TNNLS.2020.2964297
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: X-IQE

    Chen, Yixiong / Liu, Li / Ding, Chris

    eXplainable Image Quality Evaluation for Text-to-Image Generation with Visual Large Language Models

    2023  

    Abstract: This paper introduces a novel explainable image quality evaluation approach called X-IQE, which leverages visual large language models (LLMs) to evaluate text-to-image generation methods by generating textual explanations. X-IQE utilizes a hierarchical ... ...

    Abstract This paper introduces a novel explainable image quality evaluation approach called X-IQE, which leverages visual large language models (LLMs) to evaluate text-to-image generation methods by generating textual explanations. X-IQE utilizes a hierarchical Chain of Thought (CoT) to enable MiniGPT-4 to produce self-consistent, unbiased texts that are highly correlated with human evaluation. It offers several advantages, including the ability to distinguish between real and generated images, evaluate text-image alignment, and assess image aesthetics without requiring model training or fine-tuning. X-IQE is more cost-effective and efficient compared to human evaluation, while significantly enhancing the transparency and explainability of deep image quality evaluation models. We validate the effectiveness of our method as a benchmark using images generated by prevalent diffusion models. X-IQE demonstrates similar performance to state-of-the-art (SOTA) evaluation methods on COCO Caption, while overcoming the limitations of previous evaluation models on DrawBench, particularly in handling ambiguous generation prompts and text recognition in generated images. Project website: https://github.com/Schuture/Benchmarking-Awesome-Diffusion-Models

    Comment: 18 pages, 6 tables, 11 figures, NeurIPS 2023 submission
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2023-05-18
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article: Bio-driven visual saliency detection with color factor.

    Wang, Yan / Li, Teng / Wu, Jun / Ding, Chris H Q

    Frontiers in bioengineering and biotechnology

    2022  Volume 10, Page(s) 946084

    Abstract: Most visual saliency computing methods build models based on the content of an image without considering the colorized effects. Biologically, human attention can be significantly influenced by color. This study firstly investigates the sole contribution ... ...

    Abstract Most visual saliency computing methods build models based on the content of an image without considering the colorized effects. Biologically, human attention can be significantly influenced by color. This study firstly investigates the sole contribution of colors in visual saliency and then proposes a bio-driven saliency detection method with a color factor. To study the color saliency despite the contents, an eye-tracking dataset containing color images and gray-scale images of the same content is proposed, collected from 18 subjects. The CIELab color space was selected to conduct extensive analysis to identify the contribution of colors in guiding visual attention. Based on the observations that some particular colors and combinations of color blocks can attract much attention than others, the influence of colors on visual saliency is represented computationally. Incorporating the color factor, a novel saliency detection model is proposed to model the human color perception prioritization, and a deep neural network model is proposed for eye fixation prediction. Experiments validate that the proposed bio-driven saliency detection models make substantial improvements in finding informative content, and they benefit the detection of salient objects which are close to human visual attention in natural scenes.
    Language English
    Publishing date 2022-08-04
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2719493-0
    ISSN 2296-4185
    ISSN 2296-4185
    DOI 10.3389/fbioe.2022.946084
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Tensorized Bipartite Graph Learning for Multi-View Clustering.

    Xia, Wei / Gao, Quanxue / Wang, Qianqian / Gao, Xinbo / Ding, Chris / Tao, Dacheng

    IEEE transactions on pattern analysis and machine intelligence

    2023  Volume 45, Issue 4, Page(s) 5187–5202

    Abstract: Despite the impressive clustering performance and efficiency in characterizing both the relationship between the data and cluster structure, most existing graph-based multi-view clustering methods still have the following drawbacks. They suffer from the ... ...

    Abstract Despite the impressive clustering performance and efficiency in characterizing both the relationship between the data and cluster structure, most existing graph-based multi-view clustering methods still have the following drawbacks. They suffer from the expensive time burden due to both the construction of graphs and eigen-decomposition of Laplacian matrix. Moreover, none of them simultaneously considers the similarity of inter-view and similarity of intra-view. In this article, we propose a variance-based de-correlation anchor selection strategy for bipartite construction. The selected anchors not only cover the whole classes but also characterize the intrinsic structure of data. Following that, we present a tensorized bipartite graph learning for multi-view clustering (TBGL). Specifically, TBGL exploits the similarity of inter-view by minimizing the tensor Schatten p-norm, which well exploits both the spatial structure and complementary information embedded in the bipartite graphs of views. We exploit the similarity of intra-view by using the [Formula: see text]-norm minimization regularization and connectivity constraint on each bipartite graph. So the learned graph not only well encodes discriminative information but also has the exact connected components which directly indicates the clusters of data. Moreover, we solve TBGL by an efficient algorithm which is time-economical and has good convergence. Extensive experimental results demonstrate that TBGL is superior to the state-of-the-art methods. Codes and datasets are available: https://github.com/xdweixia/TBGL-MVC.
    Language English
    Publishing date 2023-03-07
    Publishing country United States
    Document type Journal Article
    ISSN 1939-3539
    ISSN (online) 1939-3539
    DOI 10.1109/TPAMI.2022.3187976
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Non-Greedy L21-Norm Maximization for Principal Component Analysis.

    Nie, Feiping / Tian, Lai / Huang, Heng / Ding, Chris

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

    2021  Volume PP

    Abstract: Principal Component Analysis (PCA) is one of the most important unsupervised methods to handle high-dimensional data. However, due to the high computational complexity of its eigen-decomposition solution, it is hard to apply PCA to the large-scale data ... ...

    Abstract Principal Component Analysis (PCA) is one of the most important unsupervised methods to handle high-dimensional data. However, due to the high computational complexity of its eigen-decomposition solution, it is hard to apply PCA to the large-scale data with high dimensionality, e.g., millions of data points with millions of variables. Meanwhile, the squared L2-norm based objective makes it sensitive to data outliers. In recent research, the L1-norm maximization based PCA method was proposed for efficient computation and being robust to outliers. However, this work used a greedy strategy to solve the eigenvectors. Moreover, the L1-norm maximization based objective may not be the correct robust PCA formulation, because it loses the theoretical connection to the minimization of data reconstruction error, which is one of the most important intuitions and goals of PCA. In this paper, we propose to maximize the L21-norm based robust PCA objective, which is theoretically connected to the minimization of reconstruction error. More importantly, we propose the efficient non-greedy optimization algorithms to solve our objective and the more general L21-norm maximization problem with theoretically guaranteed convergence. Experimental results on real world data sets show the effectiveness of the proposed method for principal component analysis.
    Language English
    Publishing date 2021-05-19
    Publishing country United States
    Document type Journal Article
    ISSN 1941-0042
    ISSN (online) 1941-0042
    DOI 10.1109/TIP.2021.3073282
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Book ; Online: Rethinking Two Consensuses of the Transferability in Deep Learning

    Chen, Yixiong / Li, Jingxian / Ding, Chris / Liu, Li

    2022  

    Abstract: Deep transfer learning (DTL) has formed a long-term quest toward enabling deep neural networks (DNNs) to reuse historical experiences as efficiently as humans. This ability is named knowledge transferability. A commonly used paradigm for DTL is firstly ... ...

    Abstract Deep transfer learning (DTL) has formed a long-term quest toward enabling deep neural networks (DNNs) to reuse historical experiences as efficiently as humans. This ability is named knowledge transferability. A commonly used paradigm for DTL is firstly learning general knowledge (pre-training) and then reusing (fine-tuning) them for a specific target task. There are two consensuses of transferability of pre-trained DNNs: (1) a larger domain gap between pre-training and downstream data brings lower transferability; (2) the transferability gradually decreases from lower layers (near input) to higher layers (near output). However, these consensuses were basically drawn from the experiments based on natural images, which limits their scope of application. This work aims to study and complement them from a broader perspective by proposing a method to measure the transferability of pre-trained DNN parameters. Our experiments on twelve diverse image classification datasets get similar conclusions to the previous consensuses. More importantly, two new findings are presented, i.e., (1) in addition to the domain gap, a larger data amount and huge dataset diversity of downstream target task also prohibit the transferability; (2) although the lower layers learn basic image features, they are usually not the most transferable layers due to their domain sensitivity.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 006 ; 004
    Publishing date 2022-12-01
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Labeled-Robust Regression: Simultaneous Data Recovery and Classification.

    Zeng, Deyu / Wu, Zongze / Ding, Chris / Ren, Zhigang / Yang, Qingyu / Xie, Shengli

    IEEE transactions on cybernetics

    2022  Volume 52, Issue 6, Page(s) 5026–5039

    Abstract: Rank minimization is widely used to extract low-dimensional subspaces. As a convex relaxation of the rank minimization, the problem of nuclear norm minimization has been attracting widespread attention. However, the standard nuclear norm minimization ... ...

    Abstract Rank minimization is widely used to extract low-dimensional subspaces. As a convex relaxation of the rank minimization, the problem of nuclear norm minimization has been attracting widespread attention. However, the standard nuclear norm minimization usually results in overcompression of data in all subspaces and eliminates the discrimination information between different categories of data. To overcome these drawbacks, in this article, we introduce the label information into the nuclear norm minimization problem and propose a labeled-robust principal component analysis (L-RPCA) to realize nuclear norm minimization on multisubspace data. Compared with the standard nuclear norm minimization, our method can effectively utilize the discriminant information in multisubspace rank minimization and avoid excessive elimination of local information and multisubspace characteristics of the data. Then, an effective labeled-robust regression (L-RR) method is proposed to simultaneously recover the data and labels of the observed data. Experiments on real datasets show that our proposed methods are superior to other state-of-the-art methods.
    MeSH term(s) Algorithms ; Principal Component Analysis
    Language English
    Publishing date 2022-06-16
    Publishing country United States
    Document type Journal Article
    ISSN 2168-2275
    ISSN (online) 2168-2275
    DOI 10.1109/TCYB.2020.3026101
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Book ; Conference proceedings: Workshop on Data Mining Using Matrices and Tensors 2009

    Ding, Chris

    (DMMT 09) ; held at SIGKDD 09 ; Paris, France, 28 June 2009

    2010  

    Institution Association for Computing Machinery
    Event/congress DMMT (2009.06.28, Paris) ; SIGKDD (Paris2009.06.28) ; Workshop on Data Mining Using Matrices and Tensors (2009.06.28, Paris)
    Author's details [ACM]. Ed.: Chris Ding,
    Language English
    Size 45 S., graph. Darst.
    Publisher Curran
    Publishing place Red Hook, NY
    Document type Book ; Conference proceedings
    ISBN 9781617382420 ; 1617382426
    Database Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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