Search Results for author:
Found 11 papers, 8 papers with code
Deep Learning-based Pupil Center Detection for Fast and Accurate Eye Tracking System
In augmented reality (AR) or virtual reality (VR) systems, eye tracking is a key technology and requires significant accuracy as well as real-time operation.
Task-Adaptive Pseudo Labeling for Transductive Meta-Learning
As a result, the proposed method is able to deal with more examples in the adaptation process than inductive ones, which can result in better classification performance of the model.
Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition
Specifically, to find pairs of similar expressions from different identities, we define the inter-feature similarity as a transportation cost.
Facial Expression Recognition
Facial Expression Recognition (FER)
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization
In addition, this paper points out the negative effects of biased features of pre-trained CNNs and emphasizes the importance of the adaptation to the target dataset.
Ranked #24 on
Anomaly Detection
on MVTec AD
Ensemble Knowledge Guided Sub-network Search and Fine-tuning for Filter Pruning
EKG utilized for the following search iteration is composed of the ensemble knowledge of interim sub-networks, i. e., the by-products of the sub-network evaluation.
Vision Transformer for Small-Size Datasets
However, the high performance of the ViT results from pre-training using a large-size dataset such as JFT-300M, and its dependence on a large dataset is interpreted as due to low locality inductive bias.
Contextual Gradient Scaling for Few-Shot Learning
Experimental results show that CxGrad effectively encourages the backbone to learn task-specific knowledge in the inner-loop and improves the performance of MAML up to a significant margin in both same- and cross-domain few-shot classification.
Interpretable Embedding Procedure Knowledge Transfer via Stacked Principal Component Analysis and Graph Neural Network
Knowledge distillation (KD) is one of the most useful techniques for light-weight neural networks.
Metric-based Regularization and Temporal Ensemble for Multi-task Learning using Heterogeneous Unsupervised Tasks
However, unsupervised multi-task learning can be biased to a specific task.
Graph-based Knowledge Distillation by Multi-head Attention Network
Knowledge distillation (KD) is a technique to derive optimal performance from a small student network (SN) by distilling knowledge of a large teacher network (TN) and transferring the distilled knowledge to the small SN.
Self-supervised Knowledge Distillation Using Singular Value Decomposition
To solve deep neural network (DNN)'s huge training dataset and its high computation issue, so-called teacher-student (T-S) DNN which transfers the knowledge of T-DNN to S-DNN has been proposed.
