. . . . . . "Supervised pretraining as alternative to episodic meta-learning for satellite imagery classification" . . . . . "Guo et al. trained Prototypical Networks episodically (40,000 episodes, approximately 3 hours). We replaced episodic training with standard supervised classification (10 epochs, approximately 15 minutes). Same backbone and image resolution. This tests whether the training method matters for cross-domain transfer, or whether the backbone features alone are sufficient." . . "We trained a ResNet-10 backbone (4.9 million parameters, 224×224 pixel images) using standard supervised classification on mini-ImageNet's 64 object categories for 10 epochs with data augmentation. At test time, we froze the backbone and used Prototypical Network-style nearest-prototype classification on EuroSAT (27,000 real Sentinel-2 satellite patches). This approach requires no meta-learning framework — only standard PyTorch classification training. Evaluation over 100 random 5-way tasks with 5, 20, and 50 labeled examples." . "Testing whether standard supervised pretraining — training a model to classify everyday objects using conventional classification — achieves comparable cross-domain few-shot accuracy on satellite imagery to the episodic meta-learning approach used by Guo et al. (2020). " . . . "Anne Fouilloux" . "2026-04-18T16:01:09.256Z"^^ . . . . . "NP created using Declaring a replication study design according to FORRT" . . "RSA" . "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" . "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" . . .