Autoencoders - EXPLAINED
Data around us, like images and documents, are very high dimensional. Autoencoders can learn a simpler representation of it. This representation can be used in many ways:
fast data transfers across a network
Self driving cars (Semantic Segmentation)
Neural Inpainting: Completing sections of an image, or removing watermarks
Latent Semantic Hashing: Clustering similar documents together.
And the list of applications goes on.
Clearly, Autoencoders can be useful. In this video, we are going to understand it's types and functions.
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REFERENCES
[1] Autoencoders: https://www.deeplearningbook.org/cont...
[2] Sparse autoencoder (last part): https://web.stanford.edu/class/cs294a...
[3] Why are sparse encoders sparse?: https://www.quora.com/Why-are-sparse-...
[4] KL Divergence: https://en.wikipedia.org/wiki/Kullbac...
[5] Semantic Hashing: https://www.cs.utoronto.ca/~rsalakhu/...
[6] Variational Autoencoders: https://jaan.io/what-is-variational-a...
[7] Xander’s video on Variational AutoEncoders (Arxiv Insights): • Variational Autoencoders
CLIPS
[1] Karol Majek’s Self driving car with RCNN: • Mask RCNN - COCO - instance segmentation
[2] Auto encoder images: https://www.jeremyjordan.me/autoencod...
[3] Semantic Segmentation with Autoencoders: https://github.com/arahusky/Tensorflo...
[4] Neural Inpainting paper: https://arxiv.org/pdf/1611.09969.pdf
[5] GAN results: • Progressive Growing of GANs for Improved Q...
#machinelearning #deeplearning #neuralnetwork #ai #datascience
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