CNN - Convolutional Neural Networks Semantic Segmentation Questions and Answers Flashcards

What is the SegNet architecture and what distinguishes its decoder from other segmentation networks?

Answer: A segmentation network whose decoder uses pooling indices from the encoder's max-pooling layers to upsample feature maps without learning upsampling weights

SegNet's decoder reuses the max-pooling indices (locations of selected values) stored by the encoder to perform non-linear upsampling, reducing memory requirements while preserving spatial structure.

What is the purpose of transposed convolutions (deconvolutions) in segmentation networks?

Answer: To learn to upsample feature maps back to a higher spatial resolution

Transposed convolutions perform a learnable upsampling by inserting zeros between input values and then applying a convolution, allowing the network to recover spatial resolution lost during downsampling.

How does the Feature Pyramid Network (FPN) benefit CNN-based segmentation models?

Answer: It builds a multi-scale feature pyramid by combining high-resolution low-level features with semantically rich high-level features via top-down pathways

FPN creates a feature pyramid where each level has strong semantics by merging high-level features (downsampled path) with high-resolution features (bottom-up path), enabling detection and segmentation at multiple scales.

What role does Dice Loss play in training semantic segmentation CNNs, particularly on imbalanced datasets?

Answer: It directly optimizes the overlap between predicted and ground-truth masks, making it robust to class imbalance

Dice Loss is derived from the Dice coefficient (2×|A∩B|/(|A|+|B|)) and directly maximizes mask overlap, giving equal weight to small foreground classes that would otherwise be dominated by the large background in cross-entropy loss.

What is the DeepLab v3+ architecture's improvement over earlier DeepLab versions?

Answer: It adds a decoder module that refines segmentation boundaries by combining low-level encoder features with the ASPP output

DeepLab v3+ introduces a simple but effective decoder that upsamples the ASPP output and concatenates it with low-level features from the encoder, sharpening object boundaries compared to simple bilinear upsampling.

Why is batch normalization particularly important in semantic segmentation CNN training?

Answer: It normalizes feature map activations across the batch to stabilize training and reduce sensitivity to weight initialization

Batch normalization normalizes activations within each mini-batch, reducing internal covariate shift and enabling higher learning rates, which is critical for the deep encoder-decoder architectures common in segmentation.