Search Results for author:
Found 19 papers, 7 papers with code
Local Correlation Consistency for Knowledge Distillation
Sufficient knowledge extraction from the teacher network plays a critical role in the knowledge distillation task to improve the performance of the student network.
Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning
The dual design enables the model to maintain the robust features of base classes, while adaptively learning distinctive feature shifts for novel classes.
Tuning-Free Visual Customization via View Iterative Self-Attention Control
Extensive experiments show the efficacy of VisCtrl across a spectrum of tasks, including personalized editing of images, videos, and 3D scenes.
Approximating arrival costs in distributed moving horizon estimation: A recursive method
Next, we extend the arrival cost design derived for linear systems to account for nonlinear systems, and a partition-based constrained distributed moving horizon estimation algorithm for nonlinear systems is formulated.
Towards Interpretable Deep Local Learning with Successive Gradient Reconciliation
its input is not reconciled with the local gradient in the previous module w. r. t.
CorDA: Context-Oriented Decomposition Adaptation of Large Language Models
For the latter, we use the instruction data from the finetuning task, such as math or coding, to orientate the decomposition and train the largest $r$ components that capture the main characteristics of the task to learn.
Data-driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large-scale nonlinear processes
The local estimators are integrated via information exchange to form a distributed estimation scheme, which provides estimates of the unmeasured/unmeasurable state variables of the original nonlinear process in a linear manner.
Partition-based distributed extended Kalman filter for large-scale nonlinear processes with application to chemical and wastewater treatment processes
In this paper, we address a partition-based distributed state estimation problem for large-scale general nonlinear processes by proposing a Kalman-based approach.
Iterative distributed moving horizon estimation of linear systems with penalties on both system disturbances and noise
In this paper, partition-based distributed state estimation of general linear systems is considered.
RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks
Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning.
GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised Learning
To tackle these challenges, we present GenView, a controllable framework that augments the diversity of positive views leveraging the power of pretrained generative models while preserving semantics.
Improving Hand Recognition in Uncontrolled and Uncooperative Environments using Multiple Spatial Transformers and Loss Functions
However, their performance deteriorates significantly in uncontrolled and uncooperative environments.
Cross-Modality Domain Adaptation for Freespace Detection: A Simple yet Effective Baseline
To better bridge the domain gap between source domain (synthetic data) and target domain (real-world data), we also propose a Selective Feature Alignment (SFA) module which only aligns the features of consistent foreground area between the two domains, thus realizing inter-domain intra-modality adaptation.
HEAD: HEtero-Assists Distillation for Heterogeneous Object Detectors
We observe that the core difficulty for heterogeneous KD (hetero-KD) is the significant semantic gap between the backbone features of heterogeneous detectors due to the different optimization manners.
Face Deblurring Based on Separable Normalization and Adaptive Denormalization
Face deblurring aims to restore a clear face image from a blurred input image with more explicit structure and facial details.
Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction
Extensive experiments on real-world data demonstrate the effectiveness of our approach.
Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space
In this paper, we examine the diversity of teacher models in the gradient space and regard the ensemble knowledge distillation as a multi-objective optimization problem so that we can determine a better optimization direction for the training of student network.
Domain Embedded Multi-model Generative Adversarial Networks for Image-based Face Inpainting
We firstly represent only face regions using the latent variable as the domain knowledge and combine it with the non-face parts textures to generate high-quality face images with plausible contents.
Detector-in-Detector: Multi-Level Analysis for Human-Parts
In particular, we adopt a region-based object detection structure with two carefully designed detectors to separately pay attention to the human body and body parts in a coarse-to-fine manner, which we call Detector-in-Detector network (DID-Net).
