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Using Deep Learning to add target effect on anything

#artificialintelligence

Using Deep Learning DC-GAN to add featured effect on anything. After my final project submission and earning a Certificate of Accomplishment in the course I just want to share with you what I did. May be this could help someone well understand and use DCGAN. For this project I chose to create an application to wear eyeglasses or hats to people without glasses or hats, using DCGAN (Deep Convolutional Generative Adversarial Networks) and hat or/and eyeglass vectors through the VGG model network we used during the course. DC-GAN uses AutoEncoder (AE) and GAN (Generative Adversarial Networks) to generate a featured output according to the input you fit in it.


Unsupervised Learning of Spoken Language with Visual Context

Neural Information Processing Systems

Humans learn to speak before they can read or write, so why can't computers do the same? In this paper, we present a deep neural network model capable of rudimentary spoken language acquisition using untranscribed audio training data, whose only supervision comes in the form of contextually relevant visual images. We describe the collection of our data comprised of over 120,000 spoken audio captions for the Places image dataset and evaluate our model on an image search and annotation task. We also provide some visualizations which suggest that our model is learning to recognize meaningful words within the caption spectrograms.


A Hybrid Radial Basis Function Neurocomputer and Its Applications

Neural Information Processing Systems

A neurocomputer was implemented using radial basis functions and a combination of analog and digital VLSI circuits. The hybrid system uses custom analog circuits for the input layer and a digital signal processing board for the hidden and output layers. The system combines the advantages of both analog and digital circuits.


A Hybrid Radial Basis Function Neurocomputer and Its Applications

Neural Information Processing Systems

A neurocomputer was implemented using radial basis functions and a combination of analog and digital VLSI circuits. The hybrid system uses custom analog circuits for the input layer and a digital signal processing board for the hidden and output layers. The system combines the advantages of both analog and digital circuits.


From Speech Recognition to Spoken Language Understanding: The Development of the MIT SUMMIT and VOYAGER Systems

Neural Information Processing Systems

Spoken input to computers, however, has yet to pass the threshold of practicality. Despite some recent successful demonstrations, current speech recognition systems typically fall far short of human capabilities of continuous speech recognition with essentially unrestricted vocabulary and speakers, under adverse acoustic environments.