Google llc (20240119256). Highly Efficient Convolutional Neural Networks simplified abstract
Contents
- 1 Highly Efficient Convolutional Neural Networks
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Highly Efficient Convolutional Neural Networks - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
Highly Efficient Convolutional Neural Networks
Organization Name
Inventor(s)
Andrew Gerald Howard of Culver City CA (US)
Mark Sandler of Mountain View CA (US)
Liang-Chieh Chen of Los Angeles CA (US)
Andrey Zhmoginov of Mountain View CA (US)
Menglong Zhu of Playa Vista CA (US)
Highly Efficient Convolutional Neural Networks - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240119256 titled 'Highly Efficient Convolutional Neural Networks
Simplified Explanation
The present disclosure introduces new, more efficient neural network architectures. One example includes a linear bottleneck layer positioned before or after convolutional layers, such as depthwise separable convolutional layers. Another example is the use of inverted residual blocks with thin bottleneck layers for input and output, and an expanded representation in between, which can include convolutional layers.
- Linear bottleneck layer before or after convolutional layers
- Inverted residual blocks with thin bottleneck layers and expanded representation
- Residual shortcut connection between thin bottleneck layers
Potential Applications
The technology can be applied in various fields such as image recognition, natural language processing, and autonomous driving systems.
Problems Solved
The neural network architectures address the challenge of improving efficiency and performance in deep learning models.
Benefits
The new architectures offer increased efficiency, improved accuracy, and faster computation speeds.
Potential Commercial Applications
Commercial applications include computer vision systems, speech recognition software, and medical image analysis tools.
Possible Prior Art
Prior art may include existing neural network architectures with similar concepts of bottleneck layers and residual connections.
Unanswered Questions
How does the performance of these new architectures compare to traditional neural networks?
The article does not provide a direct comparison between the performance of the new architectures and traditional neural networks.
Are there any limitations or drawbacks to implementing these new architectures?
The article does not mention any potential limitations or drawbacks that may arise from implementing the new neural network architectures.
Original Abstract Submitted
the present disclosure provides directed to new, more efficient neural network architectures. as one example, in some implementations, the neural network architectures of the present disclosure can include a linear bottleneck layer positioned structurally prior to and/or after one or more convolutional layers, such as, for example, one or more depthwise separable convolutional layers. as another example, in some implementations, the neural network architectures of the present disclosure can include one or more inverted residual blocks where the input and output of the inverted residual block are thin bottleneck layers, while an intermediate layer is an expanded representation. for example, the expanded representation can include one or more convolutional layers, such as, for example, one or more depthwise separable convolutional layers. a residual shortcut connection can exist between the thin bottleneck layers that play a role of an input and output of the inverted residual block.
