Review: Image Transformer

Image Generation and Super Resolution Using Transformer

Left: Super Resolution, Right: Image Generation (Input / Image Transformer Results / Ground Truth)

In this story, Image Transformer, by Google Brain, University of California, and Google AI, is briefly reviewed. In this paper:

  • Self-Attention Transformer is used for image generation.
  • By restricting the self-attention mechanism to attend to local neighborhoods, the size of images the model can process is significantly increased.

This is a paper in 2018 ICLR with over 600 citations. (Sik-Ho Tsang @ Medium)


  1. Image Transformer
  2. Local Self-Attention
  3. Experimental Results

1. Image Transformer

  • For image-conditioned generation, as in our super-resolution models, we use an encoder-decoder architecture.
  • The encoder generates a contextualized, per-pixel-channel representation of the source image.
  • The decoder autoregressively generates an output image of pixel intensities, one channel per pixel at each time step.
  • For unconditional and class-conditional generation, the Image Transformer is employed in a decoder-only configuration.
  • (Please feel free to read Transformer if interested.)

1.1. One Layer of Image Transformer

A slice of one layer of the Image Transformer
  • A slice of one layer of the Image Transformer, recomputing the representation q0 of a single channel of one pixel q by attending to a memory of previously generated pixels m1, m2, … .
  • After performing local self-attention, a two-layer position-wise feed-forward neural network is applied with the same parameters for all positions in a given layer.
  • Self-attention and the feed-forward networks are followed by dropout and bypassed by a residual connection with subsequent layer normalization.
  • The position encodings pq, p1, … are added only in the first layer.

2. Local Self-Attention

  • There are two settings for Image Transformer:
1D Local Attention and 2D Local Attention

2.1. 1D Local Attention

  • The input tensor with positional encodings is flattened in raster-scan order.
  • For each query block, the memory block M is built from the same positions as Q and an additional lm positions corresponding to pixels that have been generated before, which can result in overlapping memory blocks.

2.2. 2D Local Attention

  • The input tensor with positional encodings is partitioned into rectangular query blocks contiguous in the original image space.
  • The image is generated one query block after another, ordering the blocks in raster-scan order.

3. Experimental Results

3.1. Image Generation

Image completions from the proposed best conditional generation model
Conditional image generations for all CIFAR-10 categories
  • Unlike GAN, image generation is performed pixel by pixel which has the log likelihoods in logit space, bits per dimension can be measured.
  • Left: Images on the left are from a model that achieves 3.03 bits/dim on the test set.
  • Right: Images on the right are from the proposed best non-averaged model with 2.99 bits/dim.
  • Both models are able to generate convincing cars, trucks, and ships. Generated horses, planes, and birds also look reasonable.
Bits/dim on CIFAR-10 test and ImageNet validation sets
  • The Image Transformer outperforms all models and matches Pixel-CNN++, achieving a new state-of-the-art on ImageNet. Increasing memory block size (bsize) significantly improves performance.

3.2. Super Resolution

Four-fold super-resolution model trained on CIFAR-10, images look realistic and plausible

3.3. Super Resolution on CelebA

Negative log-likelihood and human eval performance for the Image Transformer on CelebA
Images from our 1D and 2D local attention super-resolution models trained on CelebA, sampled with different temperatures
  • τ is to tempered softmax, proposed by Dahl, 2017.
  • An 8×8 pixel image is enlarged four-fold to 32×32.
  • 35% to 36% of humans are fooled, which is significantly better than the previous state of the art.

This is a paper to utilize Transformer proposed in Attention is All You Need for image generation and image super resolution.


[2018 ICML] [Image Transformer]
Image Transformer

Single Image Super Resolution (SISR)

2014–2016: [SRCNN]
2016: [FSRCNN] [VDSR] [ESPCN] [RED-Net] [DRCN]
2017: [DnCNN] [DRRN] [LapSRN & MS-LapSRN] [MemNet] [IRCNN] [WDRN / WavResNet] [SRDenseNet] [SRGAN & SRResNet] [SelNet] [CNF] [BT-SRN] [EDSR & MDSR] [EnhanceNet]
2018: [MWCNN] [MDesNet] [RDN] [SRMD & SRMDNF] [DBPN & D-DBPN] [RCAN] [ESRGAN] [CARN] [IDN] [ZSSR] [MSRN] [Image Transformer]
2020: [PRLSR] [CSFN & CSFN-M]

My Other Previous Paper Readings



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