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Final Year Project · SCSE22-0243
FYP — Self-supervised Model for Image Prediction
NTU SCSE final-year project on self-supervised missing-patch prediction: a convolutional autoencoder with skip connections learns to restore randomly masked regions from surrounding context (CIFAR-10 style 32×32 pipeline).
Self-supervisedAutoencoderImage InpaintingTensorFlowCIFAR-10
Read thesis PDFLive demo
Select any image. A random 10×10 patch is removed, the FYP-style model fills it, and patch accuracy is reported.
Loading model…
Original
Masked
Reconstructed
Method
- Self-supervised objective: predict missing pixels from surrounding context.
- Architecture: encoder–decoder CNN with skip connections — the same structure as the FYP Keras model in
final_file_self_supervised.py. - Training setup: random 10×10 masks on 32×32 RGB images.
- Browser demo loads a lightweight retrain of that architecture (original CIFAR checkpoint wasn’t checked in) and reports patch accuracy from SSIM-style similarity + inverse MSE.