Since the first success of Dong et al., the deep-learning-based approach has
become dominant in the field of single-image super-resolution. This replaces
all the handcrafted image processing steps of traditional sparse-coding-based
methods with a deep neural network. In contrast to sparse-coding-based methods,
which explicitly create high/low-resolution dictionaries, the dictionaries in
deep-learning-based methods are implicitly acquired as a nonlinear combination
of multiple convolutions. One disadvantage of deep-learning-based methods is
that their performance is degraded for images created differently from the
training dataset (out-of-domain images). We propose an end-to-end
super-resolution network with a deep dictionary (SRDD), where a high-resolution
dictionary is explicitly learned without sacrificing the advantages of deep
learning. Extensive experiments show that explicit learning of high-resolution
dictionary makes the network more robust for out-of-domain test images while
maintaining the performance of the in-domain test images.

通过提出基于深度字典（SRDD）的端到端超分辨网络，可以具有更好的泛化性能，包括高分辨率词典的显式学习，从而在不损失深度学习优势的情况下更好地处理不在训练集中的图像。

深度字典图像超分辨率

Image Super-Resolution with Deep Dictionary

Existing image captioning models do not generalize well to out-of-domain
images containing novel scenes or objects. This limitation severely hinders the
use of these models in real world applications dealing with images in the wild.
We address this problem using a flexible approach that enables existing deep
captioning architectures to take advantage of image taggers at test time,
without re-training. Our method uses constrained beam search to force the
inclusion of selected tag words in the output, and fixed, pretrained word
embeddings to facilitate vocabulary expansion to previously unseen tag words.
Using this approach we achieve state of the art results for out-of-domain
captioning on MSCOCO (and improved results for in-domain captioning). Perhaps
surprisingly, our results significantly outperform approaches that incorporate
the same tag predictions into the learning algorithm. We also show that we can
significantly improve the quality of generated ImageNet captions by leveraging
ground-truth labels.

使用约束束搜索的方法和固定的预训练词嵌入，使得现有的深度图像描述模型能够利用图像标签器在测试阶段进行标注，从而实现新颖场景或对象的图像生成。该方法的结果表明了它在 MSCOCO 上获得了良好的表现，甚至优于采用相同标签预测的学习算法。同时，我们还证明了我们可以通过利用 ground-truth 标签来显着提高生成的 ImageNet 标题的质量。