# A Closer Look at Few-Shot Classification with Many Novel Classes
## 5 Experiments
### Experimental Setup
#### 5.1.3 Baselines  
Although FSL-MNC present a novel challenge, this paper proposes five baseline methods for comparison with the SHA-Pipeline. The comparison methods tailored for FSL-MNC include ProtoNet [35], ProtoNet-Fix, SimpleShot [21], Few-shot Baseline [3], and P \(>\) M \(>\) F [22].  
* ProtoNet [35] is widely regarded as a robust task-agnostic embedding baseline model that utilizes support set samples to form prototypes for each class. Classification is performed by computing the distances between query samples and these prototypes. This method notably emphasizes rapid adaptation to new categories without the need for specific task optimization.
* ProtoNet-Fix, a variant of ProtoNet, fixes the number of classes at five (i.e., 5-way classification) during meta-training, simplifying the training process and enhancingthe model's generalizability post-training. ProtoNet-Fix is employed to assess the impact of different meta-training strategies.
* SimpleShot [21] represents a baseline method for few-shot learning that employs L2 normalization techniques on each sample's feature vector. Its key feature lies in simplifying the processing flow: it eliminates the need for meta-learning or complex fine-tuning steps. By directly utilizing normalized feature vectors and a nearest-neighbor classifier, SimpleShot achieves impressive performance across multiple few-shot learning tasks.
* Few-shot baseline [3] focuses on optimizing model performance in few-shot settings through fine-tuning. Specifically, it fine-tunes the network backbone using cross-entropy loss applied on the support set to adapt to newly emerged classes. This method is straightforward and effective, particularly suitable for environments with strict label constraints.
* P \(>\) M \(>\) F [22] employs a cutting-edge method that utilizes ProtoNet for meta-training, building robust class prototype learning mechanisms during the meta-training phase and fine-tuning the meta-traininged backbone through data augmentation techniques in subsequent stages. This strategy not only improves the model's performance on standard testing tasks but also significantly enhances its adaptability to new domains by extending training data diversity. This method effectively bridges meta-learning and fine-tuning, optimizing the knowledge transfer from base classes to new classes. As SHA-Pipeline also employs data augmentation, P \(>\) M \(>\) F is replicated within the parallel framework of this study for experimental testing.  
The training details for these methods are as follows. To avoid over-engineered training on different datasets and architectures, the experiments adopt a generic training strategy, meta-training the backbone networks from pretrained model checkpoints (for ResNet and ViT). Although this may not yield optimal results in some cases, it simplifies the comparison process. Specifically, meta-training uses an SGD optimizer without weight decay and with a momentum of 0.9, employing a linear learning rate scaling rule [36]: \(lr=base\_lr\times\) way / 5. The learning rate adjustment uses cosine annealing with five warm-up cycles, gradually increasing from \(10^{-6}\) to \(5\times 10^{-5}\), then linearly reducing back to \(10^{-6}\). For ProtoNet, P \(>\) M \(>\) F, ProtoNet-Fix, and SHA-Pipeline, the backbone networks undergo 100 rounds of meta-training, each comprising 600 few-shot tasks. Note that the number of new classes during meta-training matches those of ProtoNet and P \(>\) M \(>\) F during meta-testing. Simplifications, including logits scaling [37], mixup [38], and label smoothing [39], are omitted. Early stopping is determined based on validation set performance. For meta-testing of P \(>\) M \(>\) F, few-shot baseline, and SHA-Pipeline, a fixed learning rate of \(10^{-6}\) is used for fine-tuning over 50 steps. SHA-Pipeline utilizes the same data augmentation as P \(>\) M \(>\) F.