[paper note] Densely Connected Convolutional Networks

• Code
• paper

Intuition

• Current trend of CNN architecture: create short paths from early layers to later layers.
  
  • ResNet
  • Highway network: The first network with more than 100 layers, bypassing paths
  • Stochastic depth: Improves the training of deep residual networks by dropping layers randomly during training, which manages to train a 1202-layer ResNet
  • FractalNets
• Wide filter is helpful.
• Connect all layers with each other.
• Combine features by concatenating them (ResNet combines by summation).
• DenseNet layers are very narrow (12 feature-maps per layer), resulting in less parameters

Model

• Dense connectivity: concatenate all the preceding layers:
  xl=Hl([x0,x1,…,xl−1])
• Composite function:
  H_l is defined as BN + ReLU + 3x3 Conv
• Pooling and dense block
  
  • See figure above
  • Transition layer between dense blocks, consist of BN + 1x1 Conv + 2x2 AveragePooing
• Growth rate k:
  
  • The number of output feature-maps.
  • The l-th layer will have k x (l-1) + k_0 input feature-maps (k_0:input image channels)
• Bottleneck layers
  
  • Introduce 1x1 Conv before 3x3 Conv to reduce number of feature-maps will improve computation efficiency.
  • H_l is changed to BN + ReLU + 1x1 Conv + BN + ReLU + 3x3 Conv
  • 1x1 Conv reduce the input to 4k feature-maps in the experiment.
• Compression
  
  • Reduce feature-maps number in transition layer by factor θ

Experiment

• Datasets
  
  • CIFAR-10/100, 32x32
    
    • Zero-padded with 4 pixels on each side
    • Randomly cropped to again produce 32×32 images
    • Half of the images are then horizontally mirrored
  • SVHN (Street View House Numbers), 32x32
  • ImageNet, 224x224, 1.2m for training, 50000 for validation, 1000 classes
• Settings: weight decay 10e-4, Nesterov momentum of 0.9 w\o dampening, learning rate 0.1 with decay scheme, dropout when no data augmentation
• Accuracy result:
  
  • 3.46% on CIFAR-10, L=190, k=40
  • 17.18% on CIFAR-100, L=190, k=40
  • 1.59% on SVHN, L=100, k=24
• Capacity: the performance continues improving when L, k increase, showing the DenseNet is less prone to overfitting (???)
• Parameter efficiency.