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Consists of variety of Autoencoders implementation for various applications such as denoising image, reverse image search, segmantic hair segmentation.

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anega006/Autoencoders

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Autoencoders

Let us see how the app interface works by inspecting its functionalities.

Here is the main dashboard.

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A brief description about the project.

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We clicked "Denoising Dirty Documents" button on the dashboard. Let's upload an image to see the output.

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The input image is denoised. We can see the clear output.

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Here is another input and its clear output.

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We clicked "Reverse Image Search" button on the dashboard. Let's upload an image to see the output.

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The reverse image search produced 5 similar images as output to "Topwear" input image.

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The reverse image search produced 5 similar images as output to "Watch" input image.

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The reverse image search produced 5 similar images as output to "Flipflop" input image.

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The reverse image search produced 5 similar images as output to "Floater" input image.

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The reverse image search produced 5 similar images as output to "Shoe" input image.

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The reverse image search produced 5 similar images as output to "Bottomwear" input image.

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The reverse image search produced 5 similar images as output to "Socks" input image.

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The reverse image search produced 5 similar images as output to "Bag" input image.

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The reverse image search produced 5 similar images as output to "Wallet" input image.

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We clicked "Semantic Hair Segmentation" button on the dashboard. Let's upload an image to see the output.

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The output is a predicted binary map and the result applied on actual image.

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The output is a predicted binary map and the result applied on another actual image.

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