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Found 8 papers, 0 papers with code
CASSOD-Net: Cascaded and Separable Structures of Dilated Convolution for Embedded Vision Systems and Applications
In this paper, we propose a network module, Cascaded and Separable Structure of Dilated (CASSOD) Convolution, and a special hardware system to handle the CASSOD networks efficiently.
Ranked #1 on
Face Detection
on ADE20K
Hardware Architecture of Embedded Inference Accelerator and Analysis of Algorithms for Depthwise and Large-Kernel Convolutions
In order to handle modern convolutional neural networks (CNNs) efficiently, a hardware architecture of CNN inference accelerator is proposed to handle depthwise convolutions and regular convolutions, which are both essential building blocks for embedded-computer-vision algorithms.
Ranked #1 on
Face Detection
on WIDER FACE
Condensation-Net: Memory-Efficient Network Architecture with Cross-Channel Pooling Layers and Virtual Feature Maps
"Lightweight convolutional neural networks" is an important research topic in the field of embedded vision.
Ranked #1 on
Face Detection
on FDDB
(Accuracy metric)
BAMSProd: A Step towards Generalizing the Adaptive Optimization Methods to Deep Binary Model
In this paper, we provide an explicit convex optimization example where training the BNNs with the traditionally adaptive optimization methods still faces the risk of non-convergence, and identify that constraining the range of gradients is critical for optimizing the deep binary model to avoid highly suboptimal solutions.
QuantNet: Learning to Quantize by Learning within Fully Differentiable Framework
Despite the achievements of recent binarization methods on reducing the performance degradation of Binary Neural Networks (BNNs), gradient mismatching caused by the Straight-Through-Estimator (STE) still dominates quantized networks.
Ranked #949 on
Image Classification
on ImageNet
IFQ-Net: Integrated Fixed-point Quantization Networks for Embedded Vision
Furthermore, based on YOLOv2, we design IFQ-Tinier-YOLO face detector which is a fixed-point network with 256x reduction in model size (246k Bytes) than Tiny-YOLO.
Ranked #10 on
Face Detection
on FDDB
Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation
Despite the recent works on knowledge distillation (KD) have achieved a further improvement through elaborately modeling the decision boundary as the posterior knowledge, their performance is still dependent on the hypothesis that the target network has a powerful capacity (representation ability).
Ranked #191 on
Image Classification
on CIFAR-10
(using extra training data)
DupNet: Towards Very Tiny Quantized CNN with Improved Accuracy for Face Detection
Firstly, we employ weights with duplicated channels for the weight-intensive layers to reduce the model size.
Ranked #1 on
Face Detection
on WIDER Face
(GFLOPs metric)
