Diagonal BiLSTM
E743715
Diagonal BiLSTM is a recurrent neural network architecture used in PixelRNN models to efficiently capture two-dimensional spatial dependencies in images by processing pixels along diagonals with bidirectional LSTMs.
Statements (45)
| Predicate | Object |
|---|---|
| instanceOf |
bidirectional LSTM variant
ⓘ
component of PixelRNN ⓘ |
| assumes | fixed raster-scan ordering of pixels ⓘ |
| belongsTo |
autoregressive generative models
ⓘ
deep learning architectures for images ⓘ |
| constrains | receptive field to previously generated pixels ⓘ |
| constrainsDependencies | to pixels in previous rows and columns ⓘ |
| contrastsWith | convolution-only autoregressive models like PixelCNN ⓘ |
| ensures | no access to future pixels in generation order ⓘ |
| hasAdvantage |
better utilization of 2D structure than simple row-wise RNNs
ⓘ
more parallel computation than fully sequential pixel RNNs ⓘ |
| hasComponent |
bidirectional passes along diagonals
ⓘ
diagonal recurrent connections ⓘ |
| hasDirection |
backward direction along diagonal
ⓘ
forward direction along diagonal ⓘ |
| hasProperty |
bidirectional processing along diagonals
ⓘ
captures long-range spatial dependencies ⓘ causal with respect to raster-scan ordering of pixels ⓘ parallelizable along image diagonals ⓘ uses LSTM gating mechanisms ⓘ |
| implementedWith | LSTM cells ⓘ |
| inputType | 2D image grid ⓘ |
| inspiredBy | sequence modeling with LSTMs ⓘ |
| introducedBy |
Aaron van den Oord
NERFINISHED
ⓘ
Koray Kavukcuoglu NERFINISHED ⓘ Nal Kalchbrenner NERFINISHED ⓘ |
| introducedIn | Pixel Recurrent Neural Networks NERFINISHED ⓘ |
| operatesOn | image pixels ⓘ |
| operationalDomain |
computer vision
ⓘ
generative modeling ⓘ |
| optimizationMethod | stochastic gradient descent variants ⓘ |
| outputType | conditional pixel distributions ⓘ |
| processes | pixels along diagonals ⓘ |
| publishedIn | ICML 2016 ⓘ |
| relatedTo |
PixelCNN
NERFINISHED
ⓘ
PixelRNN NERFINISHED ⓘ Row LSTM NERFINISHED ⓘ autoregressive density estimation for images ⓘ |
| trainingMethod | maximum likelihood estimation ⓘ |
| usedFor |
autoregressive image modeling
ⓘ
modeling two-dimensional spatial dependencies in images ⓘ |
| usedIn | PixelRNN NERFINISHED ⓘ |
| usedInTask |
image completion
ⓘ
image density modeling ⓘ image generation ⓘ |
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.