True Positive: When the model predicts the condition when it is present True Negative: When the model does not predict a condition when it is absent False Positive: When the model predicts a condition when it is absent False Negative: When the model does not predict a condition when it is ...
The primary challenge to overcome has been computational demands: the computational complexity of self-attention rises quadratically with image size. Swin transformers useshiftedwindows (instead of conventionalslidingstrides) to create non-overlapping self-attention layers, making computational complexity increa...
The paper suggests using a Transformer Encoder as a base model to extract features from the image and passing these “processed” features into a Multilayer Perceptron (MLP) head model for classification. Transformers are already very compute-heavy—infamous for their quadratic complexity when computing...
Attention mechanisms involve computing pairwise similarities between all tokens in the input sequence, resulting in quadratic complexity with respect to sequence length. This can be computationally expensive, especially for long sequences. Various techniques have been proposed to mitigate computational complex...
the entire affinity matrix\(A_{ij}\), which leads to an intractable process when the number of real patches is large. The size of\(A_{ij}\), in fact, grows linearly with the number of patches, which grows linearly with the number of images and quadratically when decreasing the stride...
Based on Eq.1and assuming a small constant change\(\delta _{ij}\), we can measure the importance of a parameter by the magnitude of the gradient\(g_{ij}\), i.e. how much does a small perturbation to that parameter change the output of the learned function for data point\(x_k\)...