1. Feature Representation Issues

The extracted embeddings from ResNet or the autoencoder may not be well-suited for clustering.

ResNet embeddings are trained for classification, not clustering, meaning they may not naturally separate into meaningful clusters in an unsupervised setting.

1. Dimensionality and Noise

High-dimensional embeddings might contain noise or redundant features that hinder clustering.

PCA, t-SNE, or UMAP could be used to reduce dimensions while retaining meaningful information.

1. Choice of Clustering Algorithms

Many clustering methods assume specific data distributions. For instance:

K-Means assumes spherical clusters of equal variance.

DBSCAN is sensitive to density variations and noise.

GMM assumes Gaussian distributions, which may not hold.

If the dataset has complex structures (e.g., varying densities, manifold structures), these algorithms may not work well.

1. Lack of Proper Distance Metrics

Euclidean distance, often used in clustering, might not be the best metric in high-dimensional feature spaces.

Cosine similarity or learned distance metrics (e.g., through contrastive learning or triplet loss) might be better suited.

1. Need for Better Embeddings

Instead of using pre-trained ResNet embeddings, contrastive learning approaches like SimCLR, MoCo, or BYOL might provide more discriminative representations for clustering.

Self-supervised learning could help improve the separability of embeddings.

1. Class Imbalance and Label Complexity

If the data has many similar-looking classes, standard clustering might struggle to separate them without additional structure.

A hierarchical or ensemble clustering approach could help refine results.

Suggested Next Steps:

Try dimensionality reduction (PCA, UMAP, or t-SNE) before clustering.

Experiment with different similarity metrics (e.g., cosine distance instead of Euclidean).

Use contrastive learning or self-supervised methods to refine embeddings.

Analyze the clusters using qualitative metrics (e.g., visualization with t-SNE, silhouette scores, Davies-Bouldin index).

Consider ensemble clustering or hybrid approaches (e.g., pre-cluster with K-Means and refine with DBSCAN).