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Found 19 papers, 0 papers with code
Enhancing Adversarial Training with Prior Knowledge Distillation for Robust Image Compression
Adversarial training has been validated in image compression models as a common method to enhance model robustness.
Bandwidth-efficient Inference for Neural Image Compression
With neural networks growing deeper and feature maps growing larger, limited communication bandwidth with external memory (or DRAM) and power constraints become a bottleneck in implementing network inference on mobile and edge devices.
Universal Learned Image Compression With Low Computational Cost
Recently, learned image compression methods have developed rapidly and exhibited excellent rate-distortion performance when compared to traditional standards, such as JPEG, JPEG2000 and BPG.
Context-aware Visual Tracking with Joint Meta-updating
These deep trackers usually do not perform online update or update single sub-branch of the tracking model, for which they cannot adapt to the appearance variation of objects.
Transformations in Learned Image Compression from a Modulation Perspective
In the view of TSM, the existing transformation methods are mathematically reduced to a linear modulation.
Exploring Structural Sparsity in Neural Image Compression
Neural image compression have reached or out-performed traditional methods (such as JPEG, BPG, WebP).
Universal Efficient Variable-rate Neural Image Compression
Recently, Learning-based image compression has reached comparable performance with traditional image codecs(such as JPEG, BPG, WebP).
Surrogate-assisted cooperative signal optimization for large-scale traffic networks
The decomposition operation significantly narrows the search space of the whole traffic network, and the surrogate-assisted optimizer greatly lowers the computational burden by reducing the number of expensive traffic simulations.
Enhancing hierarchical surrogate-assisted evolutionary algorithm for high-dimensional expensive optimization via random projection
By remarkably reducing real fitness evaluations, surrogate-assisted evolutionary algorithms (SAEAs), especially hierarchical SAEAs, have been shown to be effective in solving computationally expensive optimization problems.
A Surrogate-Assisted Variable Grouping Algorithm for General Large Scale Global Optimization Problems
SVG first designs a general-separability-oriented detection criterion according to whether the optimum of a variable changes with other variables.
TCDesc: Learning Topology Consistent Descriptors
Then we define topology distance between descriptors as the difference of their topology vectors.
An Eigenspace Divide-and-Conquer Approach for Large-Scale Optimization
Different from existing DC-based algorithms that perform decomposition and optimization in the original decision space, EDC first establishes an eigenspace by conducting singular value decomposition on a set of high-quality solutions selected from recent generations.
A Global Information Based Adaptive Threshold for Grouping Large Scale Global Optimization Problems
On the one hand, by reducing the sensitivity of the indicator in DG to the roundoff error and the magnitude of contribution weight of subcomponent, we proposed a new indicator for two variables which is much more sensitive to their interaction.
Enhancing Cooperative Coevolution for Large Scale Optimization by Adaptively Constructing Surrogate Models
As for the nonseparable sub-problems, the surrogate models are employed to evaluate the corresponding sub-solutions, and the original simulation model is only adopted to reevaluate some good sub-solutions selected by surrogate models.
Niching an Archive-based Gaussian Estimation of Distribution Algorithm via Adaptive Clustering
As a model-based evolutionary algorithm, estimation of distribution algorithm (EDA) possesses unique characteristics and has been widely applied to global optimization.
Boosting Cooperative Coevolution for Large Scale Optimization with a Fine-Grained Computation Resource Allocation Strategy
Cooperative coevolution (CC) has shown great potential in solving large scale optimization problems (LSOPs).
Surrogate Model Assisted Cooperative Coevolution for Large Scale Optimization
It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a divide-and-conquer strategy.
Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive
This study first systematically analyses the reasons for the deficiency of the traditional GEDA, then tries to enhance its performance by exploiting its evolution direction, and finally develops a new GEDA variant named EDA2.
A Bidirectional Adaptive Bandwidth Mean Shift Strategy for Clustering
The bandwidth of a kernel function is a crucial parameter in the mean shift algorithm.
