Row 8429

Row ID: 8429 | Dataset Entry | Axioma AXP Content Repository

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I'm working on a deep learning problem where the goal is to establish an injective and surjective mapping between elements of an input sequence and elements of an output sequence. The input sequence consists of feature representations of jigsaw puzzle pieces (extracted by [EfficientNetV2L-S](https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_v2_s.html), and the output elements are tuples of row, column, and rotation indices in their respective coordinate systems:

*Nmax*, *Mmax* being the number of rows and columns of the Jigsaw puzzle, and the last label describes the rotation.

* **Injective**: Each element in the input sequence (jigsaw puzzle piece) must map to a unique (row, col) index in the output sequence, ensuring that no two inputs map to the same (row, col) position. * **Surjective**: Every possible (row, col) index in the output sequence must be assigned to an input element. This ensures that the mapping covers the entire range of possible (row, col) positions, meaning all positions in the puzzle are occupied by a piece.

Fortunately, both injectivity and surjectivity go hand-in-hand for the given problem. That means that we can ensure both properties by enforcing either of them!

**Note**: I'm aware that the problem of Jigsaw Solving is probably easier solved with an algorithmic approach, though, I simply want to solve it by means of DL! Same goes for the use of a Transformer - I simply took the problem as an opportunity to work with Transformers for the first time!

I created a [Post on Stackoverflow](https://stackoverflow.com/questions/78502396/recommendations-for-ensuring-injective-and-surjective-mappings-in-deep-learning) and would appreciate a bit of guidance very much!

Thank you!

FieldValue
text I'm working on a deep learning problem where the goal is to establish an injective and surjective mapping between elements of an input sequence and elements of an output sequence. The input sequence consists of feature representations of jigsaw puzzle pieces (extracted by [EfficientNetV2L-S](https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_v2_s.html), and the output elements are tuples of row, column, and rotation indices in their respective coordinate systems: …
label r/deeplearning
dataType post
communityName r/deeplearning
datetime 2024-05-19
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Raw Record

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  "text": "I'm working on a deep learning problem where the goal is to establish an injective and surjective mapping between elements of an input sequence and elements of an output sequence.  \nThe input sequence consists of feature representations of jigsaw puzzle pieces (extracted by [EfficientNetV2L-S](https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_v2_s.html), and the output elements are tuples of row, column, and rotation indices in their respective coordinate systems:\n\n*Nmax*, *Mmax* being the number of rows and columns of the Jigsaw puzzle, and the last label describes the rotation.\n\n* **Injective**: Each element in the input sequence (jigsaw puzzle piece) must map to a unique (row, col) index in the output sequence, ensuring that no two inputs map to the same (row, col) position.\n* **Surjective**: Every possible (row, col) index in the output sequence must be assigned to an input element. This ensures that the mapping covers the entire range of possible (row, col) positions, meaning all positions in the puzzle are occupied by a piece.\n\nFortunately, both injectivity and surjectivity go hand-in-hand for the given problem. That means that we can ensure both properties by enforcing either of them!\n\n**Note**: I'm aware that the problem of Jigsaw Solving is probably easier solved with an algorithmic approach, though, I simply want to solve it by means of DL! Same goes for the use of a Transformer - I simply took the problem as an opportunity to work with Transformers for the first time!\n\nI created a [Post on Stackoverflow](https://stackoverflow.com/questions/78502396/recommendations-for-ensuring-injective-and-surjective-mappings-in-deep-learning) and would appreciate a bit of guidance very much!\n\nThank you!",
  "label": "r/deeplearning",
  "dataType": "post",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-19",
  "username_encoded": "Z0FBQUFBQm5Lakw0OGludFZrLWpXejdpeUdtS1RwMDhVbEx0Y3RBU3BIZjJ0UmJnRk1WTng5S2ktejhMQzlKZDlaUVZQUjZ4enZYbjB2Nm5CX0NjTDVuYUxYUWQzVC1fWmNLYXNaM0lzUGI2dEFiczVnb01yRTg9",
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