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Mar 13, 2022 · CaiT (Class-Attention in Image Transformers) is proposed. LayerScale significantly facilitates the convergence and improves the accuracy of image transformers at larger depths. Layers with...
CaiT, or Class-Attention in Image Transformers, is a type of vision transformer with several design alterations upon the original ViT. First a new layer scaling approach called LayerScale is used, adding a learnable diagonal matrix on output of each residual block, initialized close to (but not at) 0, which improves the training dynamics.
- Introduction
- The Layerscale Layer
- Stochastic Depth Layer
- Class Attention
- Talking Head Attention
- Feed-Forward Network
- Other Blocks
- Putting The Pieces Together: The Cait Model
- Defining Model Configuration
- Model Instantiation
In this tutorial, we implement the CaiT (Class-Attention in Image Transformers)proposed in Going deeper with Image Transformers byTouvron et al. Depth scaling, i.e. increasing the model depth for obtaining betterperformance and generalization has been quite successful for convolutional neuralnetworks (Tan et al.,Dollár et al., for example). But app...
We begin by implementing a LayerScalelayer which is one of the two modificationsproposed in the CaiT paper. When increasing the depth of the ViT models, they meet with optimization instability andeventually don't converge. The residual connections within each Transformer blockintroduce information bottleneck. When there is an increased amount of de...
Since its introduction (Huang et al.), StochasticDepth has become a favorite component in almost all modern neural network architectures.CaiT is no exception. Discussing Stochastic Depth is out of scope for this notebook. Youcan refer to this resourcein caseyou need a refresher.
The vanilla ViT uses self-attention (SA) layers for modelling how the image patches andthe learnableCLS token interact with each other. The CaiT authors propose to decouplethe attention layers responsible for attending to the image patches and the CLS tokens. When using ViTs for any discriminative tasks (classification, for example), we usuallytake...
The CaiT authors use the Talking Head attention(Shazeer et al.)instead of the vanilla scaled dot-product multi-head attention used inthe original Transformer paper(Vaswani et al.).They introduce two linear projections before and after the softmaxoperations for obtaining better results. For a more rigorous treatment of the Talking Head attention and...
Next, we implement the feed-forward network which is one of the components within aTransformer block.
In the next two cells, we implement the remaining blocks as standalone functions: 1. LayerScaleBlockClassAttention() which returns a keras.Model. It is a Transformer blockequipped with Class Attention, LayerScale, and Stochastic Depth. It operates on the CLSembeddings and the image patch embeddings. 2. LayerScaleBlock() which returns a keras.model....
Having the SA and CA layers segregated this way helps the model to focus on underlyingobjectives more concretely: 1. model dependencies in between the image patches 2. summarize the information from the image patches in a CLS token that can be used forthe task at hand Now that we have defined the CaiT model, it's time to test it. We will start by d...
Most of the configuration variables should sound familiar to you if you already know theViT architecture. Point of focus is given to sa_ffn_layers and ca_ffn_layers thatcontrol the number of SA-Transformer blocks and CA-Transformer blocks. You can easilyamend this get_config()method to instantiate a CaiT model for your own dataset.
We can successfully perform inference with the model. But what about implementationcorrectness? There are many ways to verify it: 1. Obtain the performance of the model (given it's been populated with the pre-trainedparameters) on the ImageNet-1k validation set (as the pretraining dataset wasImageNet-1k). 2. Fine-tune the model on a different datas...
However the optimization of image transformers has been little studied so far. In this work, we build and optimize deeper transformer networks for image classification. In particular, we investigate the interplay of architecture and optimization of such dedicated transformers.
DatasetModelMetric NameMetric ValueCIFAR-10CaiT-M-36 U 224Percentage correct99.4CIFAR-100CaiT-M-36 U 224Percentage correct93.1Flowers-102CaiT-M-36 U 224Accuracy99.1ImageNetCAIT-XXS-36Top 1 Accuracy82.2%Mar 31, 2021 · Moreover, our best model establishes the new state of the art on Imagenet with Reassessed labels and Imagenet-V2 / match frequency, in the setting with no additional training data. We share our code and models.
- Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, Hervé Jégou
- arXiv:2103.17239 [cs.CV]
- 2021
Class-Attention in Image Transformers. What is CaiT? CaiT, short for Class-Attention in Image Transformers, is a type of vision transformer that was designed with enhancements to the original Vision Transformer (ViT) model. Features of CaiT. As compared to ViT, CaiT uses a new layer scaling approach called LayerScale.
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What is Cait (class-attention in image Transformers)?
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Jun 8, 2021 · Paper Walkthrough: CaiT (Class-Attention in Image Transformers) In this post, I cover the paper Going deeper with image transformers by Touvron et al, which introduces LayerScale and Class-Attention Layers. Erik Storrs. Jun 8, 2021 • 3 min read.
