Search Results for author: Yikai Wang

Found 13 papers, 6 papers with code

Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks

1 code implementation ICCV 2021 Yikai Wang, Yi Yang, Fuchun Sun, Anbang Yao

In the low-bit quantization field, training Binary Neural Networks (BNNs) is the extreme solution to ease the deployment of deep models on resource-constrained devices, having the lowest storage cost and significantly cheaper bit-wise operations compared to 32-bit floating-point counterparts.


Relative Instance Credibility Inference for Learning with Noisy Labels

no code implementations29 Sep 2021 Yikai Wang, Xinwei Sun, Yanwei Fu

Specifically, we re-purpose a sparse linear model with incidental parameters as a unified Relative Instance Credibility Inference (RICI) framework, which will detect and remove outliers in the forward pass of each mini-batch and use the remaining instances to train the network.

Learning with noisy labels

Elastic Tactile Simulation Towards Tactile-Visual Perception

1 code implementation11 Aug 2021 Yikai Wang, Wenbing Huang, Bin Fang, Fuchun Sun, Chang Li

By contrast, EIP models the tactile sensor as a group of coordinated particles, and the elastic property is applied to regulate the deformation of particles during contact.

Explicit Connection Distillation

no code implementations1 Jan 2021 Lujun Li, Yikai Wang, Anbang Yao, Yi Qian, Xiao Zhou, Ke He

In this paper, we present Explicit Connection Distillation (ECD), a new KD framework, which addresses the knowledge distillation problem in a novel perspective of bridging dense intermediate feature connections between a student network and its corresponding teacher generated automatically in the training, achieving knowledge transfer goal via direct cross-network layer-to-layer gradients propagation, without need to define complex distillation losses and assume a pre-trained teacher model to be available.

Knowledge Distillation Transfer Learning

Blind signal decomposition of various word embeddings based on join and individual variance explained

no code implementations30 Nov 2020 Yikai Wang, Weijian Li

We found that by mapping different word embeddings into the joint component, sentiment performance can be greatly improved for the original word embeddings with lower performance.

Dimensionality Reduction Sentiment Analysis +1

Elastic Interaction of Particles for Robotic Tactile Simulation

no code implementations23 Nov 2020 Yikai Wang, Wenbing Huang, Bin Fang, Fuchun Sun

At its core, EIP models the tactile sensor as a group of coordinated particles, and the elastic theory is applied to regulate the deformation of particles during the contact process.

Deep Multimodal Fusion by Channel Exchanging

1 code implementation NeurIPS 2020 Yikai Wang, Wenbing Huang, Fuchun Sun, Tingyang Xu, Yu Rong, Junzhou Huang

Deep multimodal fusion by using multiple sources of data for classification or regression has exhibited a clear advantage over the unimodal counterpart on various applications.

Image-to-Image Translation Semantic Segmentation +1

LOCUS: A Novel Decomposition Method for Brain Network Connectivity Matrices using Low-rank Structure with Uniform Sparsity

no code implementations19 Aug 2020 Yikai Wang, Ying Guo

In this paper, we propose a novel blind source separation method with low-rank structure and uniform sparsity (LOCUS) as a fully data-driven decomposition method for network measures.

Resolution Switchable Networks for Runtime Efficient Image Recognition

1 code implementation ECCV 2020 Yikai Wang, Fuchun Sun, Duo Li, Anbang Yao

We propose a general method to train a single convolutional neural network which is capable of switching image resolutions at inference.

Knowledge Distillation Quantization

How to trust unlabeled data? Instance Credibility Inference for Few-Shot Learning

1 code implementation15 Jul 2020 Yikai Wang, Li Zhang, Yuan YAO, Yanwei Fu

We rank the credibility of pseudo-labeled instances along the regularization path of their corresponding incidental parameters, and the most trustworthy pseudo-labeled examples are preserved as the augmented labeled instances.

Data Augmentation Few-Shot Learning

Instance Credibility Inference for Few-Shot Learning

1 code implementation CVPR 2020 Yikai Wang, Chengming Xu, Chen Liu, Li Zhang, Yanwei Fu

To measure the credibility of each pseudo-labeled instance, we then propose to solve another linear regression hypothesis by increasing the sparsity of the incidental parameters and rank the pseudo-labeled instances with their sparsity degree.

Data Augmentation Few-Shot Image Classification

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