Search Results for author:
Found 18 papers, 6 papers with code
TFDMNet: A Novel Network Structure Combines the Time Domain and Frequency Domain Features
To get the balance between the computation complexity and memory usage, we propose a new network structure, namely Time-Frequency Domain Mixture Network (TFDMNet), which combines the advantages of both convolution layers and EMLs.
Auto-Prox: Training-Free Vision Transformer Architecture Search via Automatic Proxy Discovery
Based on the discovered zero-cost proxy, we conduct a ViT architecture search in a training-free manner.
TVT: Training-Free Vision Transformer Search on Tiny Datasets
In this paper, for the first time, we investigate how to search in a training-free manner with the help of teacher models and devise an effective Training-free ViT (TVT) search framework.
EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization
In particular, we devise an elaborate search space involving the existing proxies and perform an evolution search to discover the best correlated MQ proxy.
Progressive Meta-Pooling Learning for Lightweight Image Classification Model
Practical networks for edge devices adopt shallow depth and small convolutional kernels to save memory and computational cost, which leads to a restricted receptive field.
RD-NAS: Enhancing One-shot Supernet Ranking Ability via Ranking Distillation from Zero-cost Proxies
In this paper, we propose Ranking Distillation one-shot NAS (RD-NAS) to enhance ranking consistency, which utilizes zero-cost proxies as the cheap teacher and adopts the margin ranking loss to distill the ranking knowledge.
OVO: One-shot Vision Transformer Search with Online distillation
OVO samples sub-nets for both teacher and student networks for better distillation results.
DMFormer: Closing the Gap Between CNN and Vision Transformers
Vision transformers have shown excellent performance in computer vision tasks.
Prior-Guided One-shot Neural Architecture Search
In this paper, we present Prior-Guided One-shot NAS (PGONAS) to strengthen the ranking correlation of supernets.
Learning Convolutional Neural Networks in the Frequency Domain
The most important motivation of this research is that we can use the straightforward element-wise multiplication operation to replace the image convolution in the frequency domain based on the Cross-Correlation Theorem, which obviously reduces the computation complexity.
Multi-trial Neural Architecture Search with Lottery Tickets
Neural architecture search (NAS) has brought significant progress in recent image recognition tasks.
Inertial Proximal Deep Learning Alternating Minimization for Efficient Neutral Network Training
In recent years, the Deep Learning Alternating Minimization (DLAM), which is actually the alternating minimization applied to the penalty form of the deep neutral networks training, has been developed as an alternative algorithm to overcome several drawbacks of Stochastic Gradient Descent (SGD) algorithms.
Fixed-size Objects Encoding for Visual Relationship Detection
Instead, we propose a novel method to encode all background objects in each image by using one fixed-size vector (i. e., FBE vector).
DropFilter: A Novel Regularization Method for Learning Convolutional Neural Networks
Most of previous methods mainly consider to drop features from input data and hidden layers, such as Dropout, Cutout and DropBlocks.
Supervised Adversarial Networks for Image Saliency Detection
In the past few years, Generative Adversarial Network (GAN) became a prevalent research topic.
Learning Convolutional Neural Networks using Hybrid Orthogonal Projection and Estimation
Convolutional neural networks (CNNs) have yielded the excellent performance in a variety of computer vision tasks, where CNNs typically adopt a similar structure consisting of convolution layers, pooling layers and fully connected layers.
A Deep Learning Based Fast Image Saliency Detection Algorithm
In this paper, we propose a fast deep learning method for object saliency detection using convolutional neural networks.
Deep Learning for Object Saliency Detection and Image Segmentation
In this work, we use the computed saliency maps for image segmentation.
