# A Closer Look at Few-Shot Classification with Many Novel Classes
## 5 Experiments
### Standard Benchmark Performance  
Due to SHA-Pipeline utilizing more advanced backbone networks and additional external data, the results of this experiment are not directly comparable with many previous state-of-the-art (SOTA) algorithms. This comparison is intended to explore the effects of simple improvements, such as upgrading the feature backbone to modern network architectures and leveraging publicly available data for large-scale pretraining, against the intensive research on few-shot learning (FSL) algorithms over the past five years. As shown in Table 3, the single-domain case results, including miniImageNet and CIFAR-FS, as well as the cross-domain dataset results in Meta-Dataset (Table 4) demonstrate that despite its relatively simple framework, SHA-Pipeline surpasses the latest techniques in both intra-domain and cross-domain conditions. This underscores the efficiency and applicability of the SHA-Pipeline method. Specifically, on miniImageNet and CIFAR-FS, SHA-Pipeline achieved 5-way 5-shot accuracies of 91.8% and 93.4%, respectively, markedly higher than other methods such as ProtoNet and MetaOpt-SVM. Within the diverse categories of the Meta-Dataset, SHA-Pipeline also excelled, achieving accuracies of 95.5% and 89.2% in the Flower and Sign categories, showcasing its robust generalization capabilities when dealing with diverse and complex datasets. Additionally, compared to other algorithms using traditional models or techniques, SHA-Pipeline also exhibited significant advantages in resource utilization and computational efficiency. In the single-source benchmark tests presented in Table 3, some competitors also used external data or ImageNet pretraining to boost performance. However, the advantage displayed by SHA-Pipeline in Table 4, particularly in surpassing purely external self-supervised SOTA methods in cross-domain few-shot learning competitions, validates that even in extremely few-shot settings, SHA-Pipeline maintains exceptional performance. These results highlight the efficacy of standard cross-entropy pretraining on large network models and nearest neighbor searches based on support sets in the current field of few-shot learning, consistent with recent literature observations that for large network models, simple yet powerful pretraining combined with fine-tuned nearest neighbor searches suffices to address most few-shot learning challenges. These superior performance metrics are partly due to the advanced backbone networks and external data used by SHA-Pipeline. Compared to other classical few-shot learning algorithms, SHA-Pipeline's design and implementation offer several key improvements: (1) ProtoNet builds on forming class prototypes, which excel in quickly adapting to new categories but may not capture all necessary intra-class variations in cases with a very large number of categories or highly diverse datasets. (2) SimpleShot simplifies the classification  
Figure 9: Mean accuracy for different ways (1 and 5 shots). SHA-Pipeline (C) is SHA-Pipeline implemented by CPCC regularization and SHA-Pipeline (T) is SHA-Pipeline implemented by hierarchy triplet loss. process through L2 normalization of feature vectors, performing well in some scenarios but lacking adaptability to complex or highly nonlinear data structures. (3) The Few-shot Baseline focuses on optimizing few-shot performance through fine-tuning, a direct and effective approach, but it typically relies on the quality of initial pretraining and sensitivity to specific tasks. (4) The P > M > F method enhances adaptability by adding subsequent fine-tuning steps on top of ProtoNet. Although it improves performance, it still heavily depends on the quality of features learned during the initial meta-learning phase. In contrast, SHA-Pipeline, by integrating more external data and employing the efficient ViT-Small backbone network, not only optimizes the quality of feature learning but also significantly reduces memory and computational overhead through its unique partitioning and incremental learning strategies.