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Found 21 papers, 6 papers with code
Brain-inspired Multilayer Perceptron with Spiking Neurons
However, due to the simplicity of their structures, the performance highly depends on the local features communication machenism.
Adder Attention for Vision Transformer
With the new operation, vision transformers constructed using additions can also provide powerful feature representations.
Positive and Unlabeled Federated Learning
We study the problem of learning from positive and unlabeled (PU) data in the federated setting, where each client only labels a little part of their dataset due to the limitation of resources and time.
Federated Learning with Positive and Unlabeled Data
We study the problem of learning from positive and unlabeled (PU) data in the federated setting, where each client only labels a little part of their dataset due to the limitation of resources and time.
Learning Student Networks in the Wild
Experiments on various datasets demonstrate that the student networks learned by the proposed method can achieve comparable performance with those using the original dataset.
Data-Free Knowledge Distillation for Image Super-Resolution
Experiments on various datasets and architectures demonstrate that the proposed method is able to be utilized for effectively learning portable student networks without the original data, e. g., with 0. 16dB PSNR drop on Set5 for x2 super resolution.
Universal Adder Neural Networks
The widely-used convolutions in deep neural networks are exactly cross-correlation to measure the similarity between input feature and convolution filters, which involves massive multiplications between float values.
Winograd Algorithm for AdderNet
Adder neural network (AdderNet) is a new kind of deep model that replaces the original massive multiplications in convolutions by additions while preserving the high performance.
AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence
With a comprehensive comparison on the performance, power consumption, hardware resource consumption and network generalization capability, we conclude the AdderNet is able to surpass all the other competitors including the classical CNN, novel memristor-network, XNOR-Net and the shift-kernel based network, indicating its great potential in future high performance and energy-efficient artificial intelligence applications.
A Survey on Vision Transformer
Transformer, first applied to the field of natural language processing, is a type of deep neural network mainly based on the self-attention mechanism.
Pre-Trained Image Processing Transformer
To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs.
Ranked #1 on
Single Image Deraining
on Rain100L
(using extra training data)
AdderSR: Towards Energy Efficient Image Super-Resolution
To this end, we thoroughly analyze the relationship between an adder operation and the identity mapping and insert shortcuts to enhance the performance of SR models using adder networks.
Frequency Domain Compact 3D Convolutional Neural Networks
This paper studies the compression and acceleration of 3-dimensional convolutional neural networks (3D CNNs).
A Semi-Supervised Assessor of Neural Architectures
A graph convolutional neural network is introduced to predict the performance of architectures based on the learned representations and their relation modeled by the graph.
Distilling portable Generative Adversarial Networks for Image Translation
To promote the capability of student generator, we include a student discriminator to measure the distances between real images, and images generated by student and teacher generators.
Widening and Squeezing: Towards Accurate and Efficient QNNs
Quantization neural networks (QNNs) are very attractive to the industry because their extremely cheap calculation and storage overhead, but their performance is still worse than that of networks with full-precision parameters.
AdderNet: Do We Really Need Multiplications in Deep Learning?
The widely-used convolutions in deep neural networks are exactly cross-correlation to measure the similarity between input feature and convolution filters, which involves massive multiplications between float values.
Positive-Unlabeled Compression on the Cloud
In practice, only a small portion of the original training set is required as positive examples and more useful training examples can be obtained from the massive unlabeled data on the cloud through a PU classifier with an attention based multi-scale feature extractor.
Co-Evolutionary Compression for Unpaired Image Translation
Generative adversarial networks (GANs) have been successfully used for considerable computer vision tasks, especially the image-to-image translation.
Data-Free Learning of Student Networks
Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones and micro sensors.
Learning Student Networks via Feature Embedding
Experiments on benchmark datasets and well-trained networks suggest that the proposed algorithm is superior to state-of-the-art teacher-student learning methods in terms of computational and storage complexity.
