Illustrated Guide to Transformers Neural Network: A step by step explanation
Transformers are the rage nowadays, but how do they work? This video demystifies the novel neural network architecture with step by step explanation and illustrations on how transformers work. CORRECTIONS: The sine and cosine functions are actually applied to the embedding dimensions and time steps!
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GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton’s Paper Explained)
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Frank
- March 3, 2021
- AI
- artificial intelligence
- Arxiv
- attention mechanism
- Capsule Networks
- capsule networks explained
- column
- Computer Vision
- consensus algorithm
- Deep Learning
- deep learning tutorial
- explained
- Geoff Hinton
- geoff hinton capsule networks
- geoff hinton neural networks
- Geoffrey Hinton
- geoffrey hinton deep learning
- geoffrey hinton glom
- glom model
- Google AI
- Google Brain
- hinton glom
- introduction to deep learning
- Machine Learning
- Neural Networks
- Schmidhuber
- transformer
Yannic Kilcher covers a paper where Geoffrey Hinton describes GLOM, a Computer Vision model that combines transformers, neural fields, contrastive learning, capsule networks, denoising autoencoders and RNNs. GLOM decomposes an image into a parse tree of objects and their parts. However, unlike previous systems, the parse tree is constructed dynamically and differently for each input, […]
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TransGAN: Two Transformers Can Make One Strong GAN
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Frank
- February 19, 2021
- AI
- artificial intelligence
- Arxiv
- attention is all you need
- attention mechanism
- attention neural networks
- Deep Learning
- deep learning explained
- generative adversarial network
- local attention
- Machine Learning
- machine learning explained
- multihead attention
- Neural Networks
- paper explained
- pixelshuffle
- self attention
- superresolution
- transformer gan
- transformer gans
- transformer generative adversarial network
- transformer generator
- transgan
- vision transformer
Generative Adversarial Networks (GANs) hold the state-of-the-art when it comes to image generation. However, while the rest of computer vision is slowly taken over by transformers or other attention-based architectures, all working GANs to date contain some form of convolutional layers. This paper changes that and builds TransGAN, the first GAN where both the generator […]
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Transformers for Image Recognition at Scale
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Frank
- October 6, 2020
- AI
- andrej karpathy
- anonymous
- artificial intelligence
- Arxiv
- attention is all you need
- attention mechanism
- beyer
- big transfer
- bit
- CNN
- Convolutional Neural Network
- Data Science
- Deep Learning
- explained
- Google Brain
- google research
- iclr
- iclr 2021
- karpathy
- Machine Learning
- Neural Networks
- Paper
- peer review
- review
- TPU
- tpu v3
- transformer
- transformer computer vision
- transformer images
- under submission
- vaswani
- vision transformer
- visual transformer
- vit
Yannic Kilcher explains why transformers are ruining convolutions. This paper, under review at ICLR, shows that given enough data, a standard Transformer can outperform Convolutional Neural Networks in image recognition tasks, which are classically tasks where CNNs excel. In this Video, I explain the architecture of the Vision Transformer (ViT), the reason why it works […]
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Object-Centric Learning with Slot Attention
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Frank
- July 1, 2020
- AI
- artificial intelligence
- Arxiv
- attention
- attention mechanism
- capsules
- clevr
- CNN
- Convolutional Neural Network
- Deep Learning
- detr
- disentanglement
- embeddings
- encoder
- ethz
- explained
- gru
- LSTM
- Machine Learning
- Neural Networks
- objects
- Paper
- permutation invariant
- render
- routing
- set
- slots
- tetris
- transformer
- vision
- weight sharing
Visual scenes are often comprised of sets of independent objects. Yet, current vision models make no assumptions about the nature of the pictures they look at. Yannic Kilcher explore a paper on object-centric learning. By imposing an objectness prior, this paper a module that is able to recognize permutation-invariant sets of objects from pixels in […]
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