Augmentation_Package
This packages utilizes two augmentation methods namely.
They were taken from this paper: https://arxiv.org/abs/1910.04176
- Noise injection (aka radiation therapy): this simply upsamples a given list of embeddings with random noise.
- Delta_S: Which takes in a given array and extrapolates data given a target label.
How To Install?
!pip install SpaceAugmentation
How do i use them?
First you need to import the library and instantiate it
from aug import Augmentation
ag = Augmentation.Augmentation()Noise Injection:
l1, l2 = ag.add_noise(list_of_embeddings, list_of_labels)l1 will be a new list with doubles the size including original embeddings + new embeddings l2 will be new list of labels
Chnage the level of noise you want to inject
l1, l2 = ag.add_noise(list_of_embeddings, list_of_labels, noise_low= 0.0, nose_high= 0.1)Delta_S:
This stems from formula
X_hat =( Xi − Xj ) + Xk
Xi is random sample 1 Xj is random sample 2 Xk is random sample 3
Sample a pair of sentences (Xi, Xj) from the target category.
DELTAS applies deltas from the same target category to another sample Xk
l1, l2 = ag.delta_S(list_of_embeddings, list_of_labels, target=0)NEW !
This lambda with delta_s fusion is a novel technique that has not been tested yet or introduced yet.
if lambda_ is used then we use the lambda_ value times the delta
X_hat =( Xi − Xj ) * λ + Xk
l1, l2 = ag.delta_S(list_of_embeddings, list_of_labels, target=0, lambda_= 0.3)More Features will be added soon. Enjoy!