  Cross-domain few-shot learning (CDFSL) aims to acquire knowledge from limited
training data in the target domain by leveraging prior knowledge transferred
from source domains with abundant training samples. CDFSL faces challenges in
transferring knowledge across dissimilar domains and fine-tuning models with
limited training data. To address these challenges, we initially extend the
analysis of loss landscapes from the parameter space to the representation
space, which allows us to simultaneously interpret the transferring and
fine-tuning difficulties of CDFSL models. We observe that sharp minima in the
loss landscapes of the representation space result in representations that are
hard to transfer and fine-tune. Moreover, existing flatness-based methods have
limited generalization ability due to their short-range flatness. To enhance
the transferability and facilitate fine-tuning, we introduce a simple yet
effective approach to achieve long-range flattening of the minima in the loss
landscape. This approach considers representations that are differently
normalized as minima in the loss landscape and flattens the high-loss region in
the middle by randomly sampling interpolated representations. We implement this
method as a new normalization layer that replaces the original one in both CNNs
and ViTs. This layer is simple and lightweight, introducing only a minimal
number of additional parameters. Experimental results on 8 datasets demonstrate
that our approach outperforms state-of-the-art methods in terms of average
accuracy. Moreover, our method achieves performance improvements of up to 9\%
compared to the current best approaches on individual datasets. Our code will
be released.
