Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: HyperTransformer: Model Generation for Supervised and Semi-Supervised
  Few-Shot Learning   In this work we propose a HyperTransformer, a transformer-based model for
few-shot learning that generates weights of a convolutional neural network
(CNN) directly from support samples. Since the dependence of a small generated
CNN model on a specific task is encoded by a high-capacity transformer model,
we effectively decouple the complexity of the large task space from the
complexity of individual tasks. Our method is particularly effective for small
target CNN architectures where learning a fixed universal task-independent
embedding is not optimal and better performance is attained when the
information about the task can modulate all model parameters. For larger models
we discover that generating the last layer alone allows us to produce
competitive or better results than those obtained with state-of-the-art methods
while being end-to-end differentiable. Finally, we extend our approach to a
semi-supervised regime utilizing unlabeled samples in the support set and
further improving few-shot performance.

7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th