144 citations · 1.1k across the 52 of their papers we have counts for
20 papers · 1 filter
PTN: A Poisson Transfer Network for Semi-supervised Few-shot Learning
Huaxi Huang, Junjie Zhang, Jian Zhang +2
The predicament in semi-supervised few-shot learning (SSFSL) is to maximize the value of the extra unlabeled data to boost the few-shot learner. In this paper, we propose a Poisson…
Pre-Trained Image Processing Transformer
Hanting Chen, Yunhe Wang, Tianyu Guo +7
As the computing power of modern hardware is increasing strongly, pre-trained deep learning models (e.g., BERT, GPT-3) learned on large-scale datasets have shown their effectivenes…
Data Agnostic Filter Gating for Efficient Deep Networks
Xiu Su, Shan You, Tao Huang +5
To deploy a well-trained CNN model on low-end computation edge devices, it is usually supposed to compress or prune the model under certain computation budget (e.g., FLOPs). Curren…
Kernel Based Progressive Distillation for Adder Neural Networks
Yixing Xu, Chang Xu, Xinghao Chen +3
Adder Neural Networks (ANNs) which only contain additions bring us a new way of developing deep neural networks with low energy consumption. Unfortunately, there is an accuracy dro…
Training Binary Neural Networks through Learning with Noisy Supervision
Kai Han, Yunhe Wang, Yixing Xu +3
This paper formalizes the binarization operations over neural networks from a learning perspective. In contrast to classical hand crafted rules (\eg hard thresholding) to binarize…
SCOP: Scientific Control for Reliable Neural Network Pruning
Yehui Tang, Yunhe Wang, Yixing Xu +4
This paper proposes a reliable neural network pruning algorithm by setting up a scientific control. Existing pruning methods have developed various hypotheses to approximate the im…
Open-Set Hypothesis Transfer with Semantic Consistency
Zeyu Feng, Chang Xu, Dacheng Tao
Unsupervised open-set domain adaptation (UODA) is a realistic problem where unlabeled target data contain unknown classes. Prior methods rely on the coexistence of both source and…
Searching for Low-Bit Weights in Quantized Neural Networks
Zhaohui Yang, Yunhe Wang, Kai Han +4
Quantized neural networks with low-bit weights and activations are attractive for developing AI accelerators. However, the quantization functions used in most conventional quantiza…
AdderSR: Towards Energy Efficient Image Super-Resolution
Dehua Song, Yunhe Wang, Hanting Chen +3
This paper studies the single image super-resolution problem using adder neural networks (AdderNet). Compared with convolutional neural networks, AdderNet utilizing additions to ca…
Adversarially Robust Neural Architectures
Minjing Dong, Yanxi Li, Yunhe Wang +1
Deep Neural Networks (DNNs) are vulnerable to adversarial attacks. Existing methods are devoted to developing various robust training strategies or regularizations to update the we…
Approximated Bilinear Modules for Temporal Modeling
Xinqi Zhu, Chang Xu, Langwen Hui +2
We consider two less-emphasized temporal properties of video: 1. Temporal cues are fine-grained; 2. Temporal modeling needs reasoning. To tackle both problems at once, we exploit a…
Learning Disentangled Representations with Latent Variation Predictability
Xinqi Zhu, Chang Xu, Dacheng Tao
Latent traversal is a popular approach to visualize the disentangled latent representations. Given a bunch of variations in a single unit of the latent representation, it is expect…
A Semi-Supervised Assessor of Neural Architectures
Yehui Tang, Yunhe Wang, Yixing Xu +6
Neural architecture search (NAS) aims to automatically design deep neural networks of satisfactory performance. Wherein, architecture performance predictor is critical to efficient…
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
Zhaohui Yang, Yunhe Wang, Xinghao Chen +6
Neural Architecture Search (NAS) refers to automatically design the architecture. We propose an hourglass-inspired approach (HourNAS) for this problem that is motivated by the fact…
TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization with Few Labeled Samples
Huaxi Huang, Junjie Zhang, Jian Zhang +2
The challenges of high intra-class variance yet low inter-class fluctuations in fine-grained visual categorization are more severe with few labeled samples, \textit{i.e.,} Fine-Gra…
Automatic low-bit hybrid quantization of neural networks through meta learning
Tao Wang, Junsong Wang, Chang Xu +1
Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference, especially when deploying to edge or IoT devices with limited computat…
Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection
Jianyuan Guo, Kai Han, Yunhe Wang +5
Neural Architecture Search (NAS) has achieved great success in image classification task. Some recent works have managed to explore the automatic design of efficient backbone or fe…
K-Core based Temporal Graph Convolutional Network for Dynamic Graphs
Jingxin Liu, Chang Xu, Chang Yin +2
Graph representation learning is a fundamental task in various applications that strives to learn low-dimensional embeddings for nodes that can preserve graph topology information.…
Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks
Yehui Tang, Yunhe Wang, Yixing Xu +4
Deep neural networks often consist of a great number of trainable parameters for extracting powerful features from given datasets. On one hand, massive trainable parameters signifi…
On Positive-Unlabeled Classification in GAN
Tianyu Guo, Chang Xu, Jiajun Huang +4
This paper defines a positive and unlabeled classification problem for standard GANs, which then leads to a novel technique to stabilize the training of the discriminator in GANs.…