# A Closer Look at Few-Shot Classification with Many Novel Classes
## 5 Experiments
### Standard Benchmark Performance
This allows SHA-Pipeline to maintain high performance while also greatly enhancing operational efficiency and scalability. For example, in experiments on CIFAR-FS and miniImageNet, SHA-Pipeline exceeded other methods by at least 1% in the 5-way 5-shot setting, and in handling multiple domains in \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Training Dataset = ImageNet**} & \multicolumn{3}{c}{**In-Domain**} & \multicolumn{6}{c}{**Cross-Domain**} \\ \cline{2-13}  & **INet** & **Omglot** & **Acraft** & **CUB** & **DTD** & **QDraw** & **Fungi** & **Flower** & **Sign** & **COCO** & **Avg Acc (\%)** \\ \hline ProtoNet [28] (RN18) & 50.5 & 59.9 & 53.1 & 68.7 & 66.5 & 48.9 & 39.7 & 85.2 & 47.1 & 41.0 & 56.1 \\ AIFAA-FP-AMAML [52] (RN12) & 52.8 & 61.8 & 63.4 & 69.7 & 70.7 & 59.1 & 41.4 & 85.9 & 60.7 & 48.1 & 61.4 \\ BORIS [53] (RN18) & 51.9 & 67.5 & 54.1 & 70.6 & 68.3 & 50.3 & 41.3 & 87.3 & 51.8 & 48.0 & 59.1 \\ CTC [27] (RN34) & 62.7 & 82.2 & 79.4 & 80.6 & 75.5 & **72.6** & 51.8 & **95.3** & 82.6 & 59.9 & 74.2 \\ \hline ProtoNet (DINO/IN1K, ViT-small) & 74.3 & 80.3 & 76.0 & 84.2 & 84.9 & 69.8 & 54.2 & 93.6 & 87.9 & 62.3 & 76.8 \\ SimpleShot (DINO/IN1K, ViT-small) & 73.2 & 79.1 & 76.4 & 84.6 & 85.8 & 70.5 & 53.7 & 92.7 & 87.4 & 61.9 & 76.5 \\ few-shot-baseline (DINO/IN1K, ViT-small) & 74.3 & 80.3 & 75.6 & 83.8 & 86.6 & 71.2 & 54.8 & 94.6 & 87.0 & 61.6 & 77.0 \\ \(p>M>P\) (DINO/IN1K, ViT-small) & 74.7 & 80.7 & 76.8 & 85.0 & 86.6 & 71.3 & 54.8 & 94.6 & 88.3 & 62.6 & 77.5 \\ SHA-Pipeline (DINO/IN1K, ViT-small, CCC) & **75.4** & **81.5** & **77.5** & **85.9** & **87.5** & 72.0 & **55.3** & 95.2 & **89.2** & **63.2** & **78.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with SOTA FSL algorithms on Meta-Dataset. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method (Backbone)**} & \multirow{2}{*}{**External**} & \multirow{2}{*}{**External**} & \multirow{2}{*}{**CIFAR-FS**} & \multicolumn{3}{c}{**MiniImageNet**} \\ \cline{3-5}  & & & & **5w1s** & & **5w5s** & **5w1s** & **5w5s** \\ \hline
**Inductive** & & & & & & & \\ ProtoNet (CNN-4-64) [35] & & & & 49.4 & 68.2 & 55.5 & 72.0 \\ Baseline++ (CNN-4-64) [40] & & & & & & 48.2 & 66.4 \\ MetaOpt-SVM (ResNet12) [41] & & & & 72.0 & 84.3 & 61.4 & 77.9 \\ Meta-Baseline (ResNet12) [42] & & & & & & & 68.6 & 83.7 \\ RS-FSL (ResNet12) [43] & & ✓ & & & & 65.3 & \\
**Transductive** & & & & 76.6 & 85.8 & 65.7 & 78.4 \\ Fine-tuning (WRN-28-10) [3] & & & & 80.0 & 85.3 & 70.0 & 79.2 \\ SIB (WRN-28-10) [44] & & & & **87.7** & 90.7 & 82.9 & 88.8 \\ CTAPS + FFT (ResNet18) [46] & ✓ & ✓ & & & & 79.9 & 91.5 \\
**Self-supervised** & & & & 73.6 & 86.1 & 62.9 & 79.9 \\ ProtoNet (AMDIM ResNet) [48] & ✓ & & & & 76.8 & 91.0 \\ EPNet + SSL (WRN-28-10) [49] & ✓ & & & & & 79.2 & 88.1 \\
**Semi-supervised** & & & & & & & 70.1 & 78.7 \\ LST (ResNet12) [50] & ✓ & & & & & 70.1 & 83.7 \\ PLCM (ResNet12) [51] & ✓ & & & 80.7 & 91.8 & 92.5 & 97.7 \\ \hline ProtoNet (IN1K, IN1K, ViT-Small) & ✓ & & 80.3 & 92.1 & 92.8 & 97.2 \\ SimpleShot (IN1K, ViT-Small) & ✓ & & 80.9 & 91.9 & 92.7 & 97.4 \\ Few-shot-baseline (IN1K, ViT-Small) & ✓ & & 81.1 & 92.5 & 92.7 & 98.0 \\ SHA-Pipeline (IN1K, ViT-Small, CCC) & ✓ & & 81.9 & **93.4** & **93.6** & **98.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with represent native SOTA FSL algorithms. Methods using external data and/or labels are marked. the Meta-Dataset, it surpassed the nearest competitor by at least 1.1%, demonstrating its robustness and adaptability across various settings. These findings emphasize the importance of selecting and optimizing backbone networks and training strategies when designing few-shot learning algorithms. With the evolution of network architectures and training methods, the future of few-shot learning is anticipated to focus more on improving generalization capabilities and handling complexity through more effective data utilization and algorithmic innovations.