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Claim this profileWorks177 from public data
- Pre-Trained Image Processing Transformer2,345
To maximally excavate the capability of transformer, the IPT model is presented to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs and the contrastive learning is introduced for well adapting to different image processing tasks.
- Learning from Multiple Teacher Networks496
This paper presents a method to train a thin deep network by incorporating multiple teacher networks not only in output layer by averaging the softened outputs from different networks, but also in the intermediate layers by imposing a constraint about the dissimilarity among examples.
- Data-Free Learning of Student Networks453
A novel framework for training efficient deep neural networks by exploiting generative adversarial networks (GANs) is proposed, where the pre-trained teacher networks are regarded as a fixed discriminator and the generator is utilized for derivating training samples which can obtain the maximum response on the discriminator.
- Multi-View Intact Space Learning395
This paper proposes the Multi-view Intact Space Learning (MISL) algorithm, which integrates the encoded complementary information in multiple views to discover a latent intact representation of the data and derives the generalization error bound based on multi-view stability and Rademacher complexity.
- CARS: Continuous Evolution for Efficient Neural Architecture Search275
This work develops an efficient continuous evolutionary approach for searching neural networks that provides a series of networks with the number of parameters ranging from 3.7M to 5.1M under mobile settings and surpasses those produced by the state-of-the-art methods on the benchmark ImageNet dataset.
- Revisiting Perspective Information for Efficient Crowd Counting252
A perspective-aware convolutional neural network (PACNN) is proposed for efficient crowd counting, which integrates the perspective information into density regression to provide additional knowledge of the person scale change in an image.
- Tensor Canonical Correlation Analysis for Multi-View Dimension Reduction250
TCCA is developed, which straightforwardly yet naturally generalizes CCA to handle the data of an arbitrary number of views by analyzing the covariance tensor of the different views, and proves that the main problem of multi-view canonical correlation maximization is equivalent to finding the best rank-1 approximation of the data covariance Tensor.
- AdderNet: Do We Really Need Multiplications in Deep Learning?240
This paper develops a special back-propagation approach for AdderNets by investigating the full-precision gradient, and proposes an adaptive learning rate strategy to enhance the training procedure of Ad DerNets according to the magnitude of each neuron's gradient.
- CNNpack: packing convolutional neural networks in the frequency domain198
This work presents an effective CNN compression approach in the frequency domain, which focuses not only on smaller weights but on all the weights and their underlying connections, and relax the computational burden of convolution operations in CNNs by linearly combining the convolution responses of discrete cosine transform (DCT) bases.
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- Source-Free Domain Adaptation via Distribution Estimation175
This work proposes a novel framework called SFDA-DE to address SFDA task via source Distribution Estimation, which achieves state-of-the-art performance on multiple DA benchmarks, and even outperforms traditional DA methods which require plenty of source data.
- Multiview Matrix Completion for Multilabel Image Classification160
Experimental evaluation on two real-world data sets demonstrate the effectiveness of MVMC for transductive (semisupervised) multilabel image classification, and show that MVMC can exploit complementary properties of different features and output-consistent labels for improved multilabe image classification.
- Robust Extreme Multi-label Learning138
This paper explores and exploits an additional sparse component to handle tail labels behaving as outliers, in order to make the classical low-rank principle in multi-label learning valid and increase the scalability of the proposed algorithm.
- Hire-MLP: Vision MLP via Hierarchical Rearrangement128
Hire-MLP is presented, a simple yet competitive vision MLP architecture via Hierarchical rearrangement, which contains two levels of rearrangements to enable information communication between different regions and capture global context by circularly shifting all tokens along spatial directions.
- Decomposition-Based Transfer Distance Metric Learning for Image Classification118
This paper considers the transfer learning setting by exploiting the large quantity of side information from certain related, but different source tasks to help with target metric learning (with only a little side information), and proposes a decomposition-based transfer DML (DTDML).
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- Manifold Regularized Dynamic Network Pruning114
A new paradigm that dynamically removes redundant filters by embedding the manifold information of all instances into the space of pruned networks (dubbed as ManiDP) is proposed, which shows better performance in terms of both accuracy and computational cost compared to the state-of-the-art methods.
- Large Margin Multi-Modal Multi-Task Feature Extraction for Image Classification106
A novel large margin multi- modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification that utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality.
- Cost-Sensitive Feature Selection by Optimizing F-Measures105
An effective feature selection algorithm that explores the class imbalance issue by optimizing F-measures and selects features corresponding to the best F-measure, which will fully represent the properties of all classes.
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- Distilling Portable Generative Adversarial Networks for Image Translation89
Qualitative and quantitative analysis by conducting experiments on benchmark datasets demonstrate that the proposed method can learn portable generative models with strong performance.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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