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Found 8 papers, 4 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.
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).
