In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[Q]: Training deep neural networks requires many training samples, but in practice training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing. This creates a fundamental quality- versus-quantity trade-off in the learning process. Do we learn from the small amount of high-quality data or the potentially large amount of weakly-labeled data? We argue that if the learner could somehow know and take the label-quality into account when learning the data representation, we could get the best of both worlds. To this end, we propose "fidelity-weighted learning" (FWL), a semi-supervised student- teacher approach for training deep neural networks using weakly-labeled data. FWL modulates the parameter updates to a student network (trained on the task we care about) on a per-sample basis according to the posterior confidence of its label-quality estimated by a teacher (who has access to the high-quality labels). Both student and teacher are learned from the data. We evaluate FWL on two tasks in information retrieval and natural language processing where we outperform state-of-the-art alternative semi-supervised methods, indicating that our approach makes better use of strong and weak labels, and leads to better task-dependent data representations.
[A]: We propose Fidelity-weighted Learning, a semi-supervised teacher-student approach for training neural networks using weakly-labeled data.

[Q]: Plan recognition aims to look for target plans to best explain the observed actions based on plan libraries and/or domain models. Despite the success of previous approaches on plan recognition, they mostly rely on correct action observations.
Recent advances in visual activity recognition have the potential of enabling applications such as automated video surveillance. Effective approaches for such problems would require the ability to recognize the plans of agents from video information. Traditional plan recognition algorithms rely on access to detailed planning domain models. One recent promising direction involves learning approximate (or shallow) domain models directly from the observed activity sequences. Such plan recognition approaches expect observed action sequences as inputs. However, visual inference results are often noisy and uncertain, typically represented as a distribution over possible actions. In this work, we develop a visual plan recognition framework that recognizes plans with an approximate domain model learned from uncertain visual data.
[A]: Handling Uncertainty in Visual Perception for Plan Recognition

[Q]: While deep neural networks have shown outstanding results in a wide range of applications,
learning from a very limited number of examples is still a challenging
task. Despite the difficulties of the few-shot learning, metric-learning techniques
showed the potential of the neural networks for this task. While these methods
perform well, they don't provide satisfactory results. In this work, the idea of
metric-learning is extended with Support Vector Machines (SVM) working mechanism,
which is well known for generalization capabilities on a small dataset.
Furthermore, this paper presents an end-to-end learning framework for training
adaptive kernel SVMs, which eliminates the problem of choosing a correct kernel
and good features for SVMs. Next, the one-shot learning problem is redefined
for audio signals. Then the model was tested on vision task (using Omniglot
dataset) and speech task (using TIMIT dataset) as well. Actually, the algorithm
using Omniglot dataset improved accuracy from 98.1% to 98.5% on the one-shot
classification task and from 98.9% to 99.3% on the few-shot classification task.
[A]:
The proposed method is an end-to-end neural SVM, which is optimized for few-shot learning.