AML Natural Language Processing & Computer Vision Flashcards

What is transfer learning in the context of computer vision?

Answer: Using a pre-trained model's weights as a starting point for a new task

Transfer learning reuses the feature representations learned from a large dataset (e.g., ImageNet) to improve performance on a smaller target task.

Which technique is used to augment training images by randomly flipping, rotating, or cropping them?

Answer: Data augmentation

Data augmentation artificially expands the training set with label-preserving transformations, reducing overfitting in vision models.

In NLP, what is tokenization?

Answer: Splitting raw text into individual units such as words or subwords

Tokenization breaks raw text into discrete units (tokens) such as words, subwords, or characters that a model can process.

What is the attention mechanism in transformer models primarily designed to do?

Answer: Allow the model to weigh the relevance of different input tokens when producing each output

Attention computes a weighted sum of value vectors, where weights reflect how relevant each input token is to the current output position.

Which metric is commonly used to evaluate object detection models by measuring overlap between predicted and ground-truth bounding boxes?

Answer: Intersection over Union (IoU)

IoU divides the area of overlap between the predicted and actual bounding boxes by their combined union area, ranging from 0 to 1.

What is the BLEU score used to measure in NLP?

Answer: Quality of machine-generated text by comparing n-gram overlap with reference translations

BLEU measures how many n-grams in a machine translation match those in one or more reference translations, normalized by length.