SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Training settings. For a fair comparison, we use ResNet-18 [25] as the backbone for all methods. Similar to [7], we pre-train the backbone using the unsupervised Spectral Contrastive Learning [23] for 1200 epochs. The configuration for the pre-training stage is consistent with [23]. Note that the pre-training stage does not incorporate any label information. At the training stage, we follow the same practice in [7, 63], and train our model  $f(\cdot)$  by only updating the parameters of the last block of ResNet. In addition, we add a trainable two-layer MLP projection head that projects the feature from the penultimate layer to an embedding space  $\mathbb{R}^k$  ( $k = 1000$ ). We use the same data augmentation strategies as SimSiam [12, 23]. For CIFAR-10, we set  $\eta_l = 0.25$ ,  $\eta_u = 1$  with training epoch 100, and we evaluate using features extracted from the layer preceding the projection. For CIFAR-100, we set  $\eta_l = 0.0225$ ,  $\eta_u = 3$  with 400 training epochs and assess based on the projection layer's features. We use SGD with momentum 0.9 as an optimizer with cosine annealing (lr=0.05), weight decay 5e-4, and batch size 512.
Evaluation settings. At the inference stage, we evaluate the performance in a transductive manner (evaluate on  $\mathcal{D}_u$ ). We run a semi-supervised K-means algorithm as proposed in [69]. We follow the evaluation strategy in [7] and report the following metrics: (1) classification accuracy on known classes, (2) clustering accuracy on the novel data, and (3) overall accuracy on all classes. The accuracy of the novel classes is measured by solving an optimal assignment problem using the Hungarian algorithm [36]. When reporting accuracy on all classes, we solve optimal assignments using both known and novel classes.