Few-Shot Image Recognition by Predicting Parameters from Activations

In this paper, we are interested in the few-shot learning problem. In particular, we focus on a challenging scenario where the number of categories is large and the number of examples per novel category is very limited, e.g. 1, 2, or 3. Motivated by the close relationship between the parameters and the activations in a neural network associated with the same category, we propose a novel method that can adapt a pre-trained neural network to novel categories by directly predicting the parameters from the activations. Zero training is required in adaptation to novel categories, and fast inference is realized by a single forward pass. We evaluate our method by doing few-shot image recognition on the ImageNet dataset, which achieves the state-of-the-art classification accuracy on novel categories by a significant margin while keeping comparable performance on the large-scale categories. We also test our method on the MiniImageNet dataset and it strongly outperforms the previous state-of-the-art methods

A Possibility of Search for New Physics at LHCb

It is interesting to search for new physics beyond the standard model at LHCb. We suggest that weak decays of doubly charmed baryon such as $\Xi_{cc}(3520)^+, \Xi_{cc}^{++}$ to charmless final states would be a possible signal for new physics. In this work, we consider two models, i.e. the unparticle and $Z'$ as examples to study such possibilities. We also discuss the cases for $\Xi^0_{bb}, \Xi_{bb}^-$ which have not been observed yet, but one can expect to find them when LHCb begins running. Our numerical results show that these two models cannot result in sufficiently large decay widths, therefore if such modes are observed at LHCb, there must be a new physics other than the unparticle or $Z'$ models.Comment: 7 pages, 3 figures, 1 table. More references and discussion adde