Reinforcement learning & computer vision.

Tasks performed

Based on the graph representations and Q-learning concepts from the referenced paper, I implemented experimental code that connects the TSP environment, model, training loop, and insertion rules.

Results

The program takes city locations, distances and visit status as graph input and uses an insertion heuristic to add the city chosen by the model to the route. Comparing the public code against baselines, I also traced why the learning did not work as intended.

• Step 2 AI city selection + heuristic route insertion

Tasks performed

Implemented code for clothing classification using a pre-trained ResNet50V2 model, along with a workflow for class-based sampling and evaluation; misclassifications were analyzed using a confusion matrix.

Results

Built a training and analysis pipeline that classifies K-Fashion photos into dresses, outerwear, pants and tops. Later, on the public Fashion-MNIST dataset, I changed settings one at a time to separate how data augmentation and the shortcut connection affect predictions.

• 4 types Classification: Dresses, outerwear, pants, and tops

Tasks performed

Configured input preprocessing and clothing masks for a virtual try-on model; integrated a Flask upload API with an Android client built using Kotlin and Retrofit.

Results

We developed an integrated workflow that takes a person's photo and a selected garment, performs preprocessing, generates an image of the person wearing the item, and returns the result to the app. We reused existing generative models as well as pose and body-parsing models; our own implementation focused on input configuration, mask processing, and the integration between the server and the app.

• Step 3 Pre-processing → Generate "try-on" image → Return to app