# A Closer Look at Few-Shot Classification with Many Novel Classes
## 5 Experiments
### Visualization Study  
On the MNIST and CIFAR-100 datasets, this study applied t-SNE to reduce and visualize image features. The image features were reduced to a two-dimensional space and projected using t-SNE for display purposes.  
From Figure 11a, it is evident that different categories of digits are projected into distinct regions. Digits of the same category (i.e., points representing the same character) mostly cluster together, forming clear clusters. There are distinct boundaries between clusters, indicating that the feature vectors can effectively differentiate between categories. The clustering effect demonstrates that the model's extracted features possess good discriminative power, with digits from different categories well-separated, reflecting the representativeness and effectiveness of the feature vectors in classification.  
The CIFAR-100 dataset is more complex, containing more categories and higher image complexity, including labels at different levels of granularity. The CIFAR-100 dataset consists of 20 superclasses encompassing a total of 100 subclasses, with each image labeled with both coarse- and fine-grained categories. From Figure 11b, images of different categories are projected into distinct color areas, each color representing a different level of hierarchical clustering. Similar to the MNIST dataset, images of the same superclass (i.e., points of the same color) also cluster together. However, due to the complexity of the CIFAR-100 dataset, the clustering boundaries are not as clear as those in the MNIST dataset. Despite the more dispersed distribution of feature vectors due to the dataset's greater number of categories and more complex image backgrounds, an overall clustering effect is still observable. The clustering effect is slightly blurred, but the overall trend still reflects the distinctions between categories. Notably, in the CIFAR-100 dataset, different colors  
Figure 11: Hierarchical clustering visualization by t-SNE. Features are extracted from the MNIST and CIFAR-100 datasets using the fine-tuned SHA-Pipeline backbone network (ViT-Small, DINO).  
represent different hierarchical clustering results, indicating that the model captures the hierarchical structure among categories effectively. This suggests that SHA-Pipeline's clustering algorithm helps the model better understand and utilize the relationships between categories, thereby enhancing classification performance.  
Through the visualization of features from the MNIST and CIFAR-100 datasets, the following conclusions can be drawn: the visualization results after t-SNE projection demonstrate that the model's extracted features perform well in distinguishing between different categories, particularly evident in the MNIST dataset. Due to the diversity of categories and complexity of images, the clustering effect in CIFAR-100 is slightly inferior to that in the MNIST dataset, but clear category clustering effects are still evident. The visualization results across different datasets showcase the model's feature extraction capabilities in handling both simple and complex datasets, validating the model's generalizability and robustness. These visual results not only confirm the effectiveness of the model in few-shot learning tasks but also provide important references for further optimization of the model.