This project is focus on evaluating two to three recent approaches to achive scale equivariance and/or invariance of CNNs.

1. Locally Scale-Invariant Convolutional Neural Network
   • Method: Firstly, they applies filters at multiple scales in each layer so a single filter can detect and learn patterns at multiple scales. Then, max-pool responses over scales to obtain representations that are locally scale invariant yet have the same dimensionality as a traditional ConvNet layer output.
   • Dataset: MNIST-Scale
2. Scale Steerable Filters for Locally Scale-Invariant Convolutional Neural Networks
   • Method: Using the log-radial harmonics as a complex steerable basis, we construct a lo- cally scale invariant CNN, where the filters in each convolution layer are a linear combination of the basis filters.
   • Dataset: MNIST-Scale
3. Making Convolutional Network Shift-Invariant Again
   • Method: Antialiasing filter combined with subsampling, for example, max pooling and CNN with stride.
   • Dataset: MNIST-Scale

• 11 Nov - 24 Nov:

  • Write the summary of Locally Scale-Invariant Convolutional Neural Network.
  • Implement the results of Locally Scale-Invariant Convolutional Neural Network on MNIST-Scale dataset.

• 25 Nov - 08 Dec:

  • Write the summary of Scale Steerable Filters for Locally Scale-Invariant Convolutional Neural Networks.
  • Implement the results of Scale Steerable Filters for Locally Scale-Invariant Convolutional Neural Networks on MNIST-Scale.

• 09 Dec - 22 Dec:

  • Write the summary of Making Convolutional Network Shift-Invatiant Again
  • Combine the method with SS-CNN, denoted as SS-CNN-BlurPool
  • Evaluate the method on MNIST-Scale.
  • Implement the baseline CNN on MNIST-Scale
  • Compare the results of CNN, SS-CNN, SI-ConvNet, and SS-CNN-BlurPool.

• 23 Dec - -5 Jan:

  • Preproccessing with dataset Oral Cancer
  • Evaluation on different training size

• 06 Jan - 12 Jan:

  • Write the report.
  • Design poster.