• Due to COVID-19, we will continue online lectures with pre-recorded video lectures for some time.
  For each chapter, please download PPT(Lecture Video), and play it in slideshow mode.

• Starting from Lecture 1, after watching each lecture video, each student needs to submit
  a lecture summary via Google Classroom. Check your email to find the Google Classroom
  link of CBNU/Intelligent Image Recognition. 

 

• Lecture0: Course Introduction [PPT (Lecture Video)]​​

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• Lecture1: Introduction To Image Recognition [PPT (Lecture Video)]

• Lecture2: Image Classification [PPT (Lecture Video)]

• Lecture3: Convolutional Neural Network [PPT (Lecture Video)]

• Lecture4: Training with Backpropagation [PPT (Lecture Video)]

• Lecture5: CNN Architectures [PPT (Lecture Video)]

• Lecture6: Segmentation and Detection [PPT (Lecture Video)]

• Lecture7: Fast Detection CNNs [PPT (Lecture Video)]

• Lecture8: Generative Models [PPT (Lecture Video)]

• Lecture9: Generative Adversarial Networks [PPT (Lecture Video)]​

• Final Exam: December 6th (Monday) 7PM~9PM, Location: E8-7 Room 307,
  Open Note (Only Lecture Notes are allowed during the exam)

• Term Project: Presentation on December 13th (Monday) 7PM~9PM, E10 Room 406

  • All students who are studying deep learning must select Option (1).
    Students whose major is not deep learning can select Option (2) with the professor's approval.

  • (1) Train a CNN model of your choice using a public dataset or your own dataset. Demonstrate the inference and accuracy (You can choose the same CNN as your research topic)

  • (2) Paper Survey on CNNs or GANs for classification, detection, segmentation, augmentation, and present the summary of your survey result. 

• Overall Evaluation Results [Evaluation Scores]​

  • ​If you have any questions regarding your scores, please send me an inquiry email.

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