[ICML 2024] Isometric Representation Learning for Disentangled Latent Space of Diffusion Models

[2024 Team 13] Advancing Image Reconstruction: Evaluating BOLD & Beta Signals in fMRI Brain Decoding

[2024 Team 12] Scene Text Detection in Rainy Day

[2024 Team 11] Representing Dynamic Scenes using Physics-integrated Gaussian Splatting

[2024 Team 10] Fine-grained Food Image Classification of Korean Foods

[2024 Team 9] UniBench: Fully Automated Evaluation of Image Editing Models

[2024 Team 8] STATDM: Scene Text Aware style Transfer Diffusion Model

[2024 Team 7] Transparent Object Reconstruction in 3D Gaussian Splatting with RGB-to-TIR Translation

[2024 Team 6] SkEncode: Sketch Encoder for Sketch-to-video

[2024 Team 5] SummDiff: Leveraging Generative Diffusion to Video Summarization

[2024 Team 4] Inferability-guided Learning for Person Re-identification

[2024 Team 3] Hierarchical Image Geolocalization Using ViT-based Encoders and Satellite Map Images

[2024 Team 2] Exploring Shape and Texture Bias in Object Detection

[2024 Team 1] Bridging image manipulation localization and generated image detection

[ML/DL] Lab 4. Sequential Data Modeling (RNN & Transformers)

[ML/DL] Lecture 20: Transformers II

[ML/DL] Lecture 19. Attention Mechanism & Transformers I

[ML/DL] Lecture 18. Recurrent Neural Networks II (LSTMs & Seq2seq Models)

[ML/DL] Lecture 17. Sequential Data & Recurrent Neural Networks I

[ML/DL] Lab 3. Introduction to PyTorch & Neural Networks Basics

[ML/DL] Lecture 16. Training Neural Networks II

[ML/DL] Lecture 13. Neural Networks & Backpropagation

[ML/DL] Lab 2

[ML/DL] Lecture 12. Nearest Neighbors & Softmax Classifiers

[ML/DL] Lecture 11. Unsupervised Learning

[ML/DL] Lecture 10. Support Vector Machines

[ML/DL] Lecture 9. Ensemble Methods and Boosting

[ML/DL] Lecture 8. Decision Trees

[ML/DL] Lab 1. Numpy & Supervised Learning

[ML/DL] Lecture 7. Overfitting & Regularization