[HTML][HTML] Review of image classification algorithms based on convolutional neural networks
L Chen, S Li, Q Bai, J Yang, S Jiang, Y Miao - Remote Sensing, 2021 - mdpi.com
Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …
emergence of deep learning has promoted the development of this field. Convolutional …
Neural architecture search survey: A hardware perspective
We review the problem of automating hardware-aware architectural design process of Deep
Neural Networks (DNNs). The field of Convolutional Neural Network (CNN) algorithm design …
Neural Networks (DNNs). The field of Convolutional Neural Network (CNN) algorithm design …
Visual transformers: Token-based image representation and processing for computer vision
Computer vision has achieved remarkable success by (a) representing images as uniformly-
arranged pixel arrays and (b) convolving highly-localized features. However, convolutions …
arranged pixel arrays and (b) convolving highly-localized features. However, convolutions …
Searching for mobilenetv3
We present the next generation of MobileNets based on a combination of complementary
search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile …
search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile …
Dreaming to distill: Data-free knowledge transfer via deepinversion
We introduce DeepInversion, a new method for synthesizing images from the image
distribution used to train a deep neural network. We" invert" a trained network (teacher) to …
distribution used to train a deep neural network. We" invert" a trained network (teacher) to …
Single path one-shot neural architecture search with uniform sampling
We revisit the one-shot Neural Architecture Search (NAS) paradigm and analyze its
advantages over existing NAS approaches. Existing one-shot method, however, is hard to …
advantages over existing NAS approaches. Existing one-shot method, however, is hard to …
Squeezesegv3: Spatially-adaptive convolution for efficient point-cloud segmentation
LiDAR point-cloud segmentation is an important problem for many applications. For large-
scale point cloud segmentation, the de facto method is to project a 3D point cloud to get a …
scale point cloud segmentation, the de facto method is to project a 3D point cloud to get a …
Neural architecture search on imagenet in four gpu hours: A theoretically inspired perspective
Neural Architecture Search (NAS) has been explosively studied to automate the discovery of
top-performer neural networks. Current works require heavy training of supernet or intensive …
top-performer neural networks. Current works require heavy training of supernet or intensive …
[HTML][HTML] Estimation of energy consumption in machine learning
Energy consumption has been widely studied in the computer architecture field for decades.
While the adoption of energy as a metric in machine learning is emerging, the majority of …
While the adoption of energy as a metric in machine learning is emerging, the majority of …
Fbnetv2: Differentiable neural architecture search for spatial and channel dimensions
Abstract Differentiable Neural Architecture Search (DNAS) has demonstrated great success
in designing state-of-the-art, efficient neural networks. However, DARTS-based DNAS's …
in designing state-of-the-art, efficient neural networks. However, DARTS-based DNAS's …