- Fashion Label Classification using CNN:
- A Convolutional Neural Network (CNN) model was developed for fashion label classification.
- Through hyperparameter tuning, the network architecture was optimized, resulting in a test accuracy of 94%.
- Identifying Mystery Labels using Intermediate Layer Encodings:
- Encodings from the intermediate layer of a CNN model were utilized to uncover unidentified labels within the dataset.
- Dimensionality reduction techniques like Principal Component Analysis (PCA) were applied to reduce the dimensionality of the encodings.
- Clustering algorithms such as K-means and DBSCAN were employed for unsupervised classification, enabling grouping of similar encodings. This process facilitated the identification and classification of the mystery labels in the dataset.
- Feature Extraction and Representation Analysis:
- PCA and Autoencoder were compared for dimensionality reduction to analyze the effectiveness of feature extraction techniques.
- Autoencoder was found to generate the most effective representation, as evidenced by improved classification test accuracy when combined with a K-nearest neighbors (KNN) classifier. This suggests that the Autoencoder model extracted more informative and discriminative features for accurate classification.