#1
Which deep learning architecture is often used for image classification tasks?
ResNet
ExplanationResNet uses residual connections to enable training of very deep neural networks for image classification.
#2
What is the role of the activation function in a neural network?
To introduce non-linearity to the network
ExplanationActivation functions enable neural networks to model complex relationships and patterns.
#3
Which technique is commonly used for image denoising?
Gaussian blur
ExplanationGaussian blur is a smoothing technique used to reduce noise and enhance image quality.
#4
Which deep learning framework is commonly used for computer vision tasks?
TensorFlow
ExplanationTensorFlow is a widely used deep learning framework, particularly in computer vision applications.
#5
What does the acronym CNN stand for in the context of deep learning?
Convolutional Neural Network
ExplanationCNNs are specialized neural networks designed for processing grid-like data, commonly used in image analysis.
#6
What is the basic purpose of image segmentation in computer vision?
To divide an image into meaningful segments
ExplanationSegmentation helps identify and analyze distinct regions within an image.
#7
Which algorithm is commonly used for object detection in images?
YOLO (You Only Look Once)
ExplanationYOLO efficiently detects objects by dividing the image into a grid and making predictions simultaneously.
#8
In image processing, what does the term 'Histogram' represent?
A graphical representation of pixel intensities in an image
ExplanationHistograms illustrate the distribution of pixel intensities, aiding in image analysis.
#9
Which technique is commonly used for image feature extraction?
Principal Component Analysis (PCA)
ExplanationPCA reduces image dimensionality by capturing essential features and patterns.
#10
Which type of neural network architecture is suitable for sequential data, such as time series or text?
Recurrent Neural Network (RNN)
ExplanationRNNs process sequential data by maintaining a hidden state, allowing them to capture temporal dependencies.
#11
What is the purpose of non-maximum suppression in object detection?
To reduce false positives and keep only the most confident predictions
ExplanationNon-maximum suppression filters out redundant bounding box predictions.
#12
What is the significance of anchor boxes in object detection algorithms?
To define the spatial locations of potential objects in an image
ExplanationAnchor boxes help anchor the bounding box predictions at different scales and aspect ratios.
#13
What is the purpose of data augmentation in image processing?
To increase the size of the training dataset by applying transformations
ExplanationData augmentation diversifies the training dataset, improving model generalization.
#14
What is the primary advantage of transfer learning in image classification?
It enables the reuse of pre-trained models on a new task
ExplanationTransfer learning leverages pre-trained models, saving training time and resources.
#15
In the context of image recognition, what does the term 'F1 score' measure?
Precision and recall balance
ExplanationF1 score balances precision and recall, providing a comprehensive evaluation of a classifier's performance.