The ultimate goal of transfer learning is to reduce labeled data requirements
by exploiting a pre-existing embedding model trained for different datasets or
tasks. The visual and language communities have established benchmarks to
compare embeddings, but the speech community has yet to do so. This paper
proposes a benchmark for comparing speech representations on non-semantic
tasks, and proposes a representation based on an unsupervised triplet-loss
objective. The proposed representation outperforms other representations on the
benchmark, and even exceeds state-of-the-art performance on a number of
transfer learning tasks. The embedding is trained on a publicly available
dataset, and it is tested on a variety of low-resource downstream tasks,
including personalization tasks and medical domain. The benchmark, models, and
evaluation code are publicly released.

本篇文章提出了对非语义任务进行语音表征对比的基准，并提出了一种基于无监督三元组损失目标的表征方法，该方法在基准测试中表现优异，甚至在许多迁移学习任务中超越了最新技术水平；

学习语音的通用非语义表示

Towards Learning a Universal Non-Semantic Representation of Speech

Automated dermoscopic image analysis has witnessed rapid growth in diagnostic
performance. Yet adoption faces resistance, in part, because no evidence is
provided to support decisions. In this work, an approach for evidence-based
classification is presented. A feature embedding is learned with CNNs,
triplet-loss, and global average pooling, and used to classify via kNN search.
Evidence is provided as both the discovered neighbors, as well as localized
image regions most relevant to measuring distance between query and neighbors.
To ensure that results are relevant in terms of both label accuracy and human
visual similarity for any skill level, a novel hierarchical triplet logic is
implemented to jointly learn an embedding according to disease labels and
non-expert similarity. Results are improved over baselines trained on disease
labels alone, as well as standard multiclass loss. Quantitative relevance of
results, according to non-expert similarity, as well as localized image
regions, are also significantly improved.

提出一种基于证据的分类方法，利用 CNN，三元损失和全局平均池化学习特征嵌入，通过 kNN 搜索分类，并以发现的邻居和最相关的局部图像区域作为证据。通过实现一种新的分级三元逻辑，根据疾病标签和非专家相似性一起学习嵌入，以确保结果对于任何技能水平的人来说都具有标签准确性和视觉相似性，从而改善了基于疾病标签和标准多类损失的基准结果，并显着改善了结果的定量相关性和局部图像区域。

协作人工智能（CHAI）：基于证据的皮肤镜图像的可解释性黑色素瘤分类

Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma  Classification in Dermoscopic Images

In the same vein of discriminative one-shot learning, Siamese networks allow
recognizing an object from a single exemplar with the same class label.
However, they do not take advantage of the underlying structure of the data and
the relationship among the multitude of samples as they only rely on pairs of
instances for training. In this paper, we propose a new quadruplet deep network
to examine the potential connections among the training instances, aiming to
achieve a more powerful representation. We design four shared networks that
receive multi-tuple of instances as inputs and are connected by a novel loss
function consisting of pair-loss and triplet-loss. According to the similarity
metric, we select the most similar and the most dissimilar instances as the
positive and negative inputs of triplet loss from each multi-tuple. We show
that this scheme improves the training performance. Furthermore, we introduce a
new weight layer to automatically select suitable combination weights, which
will avoid the conflict between triplet and pair loss leading to worse
performance. We evaluate our quadruplet framework by model-free
tracking-by-detection of objects from a single initial exemplar in several
Visual Object Tracking benchmarks. Our extensive experimental analysis
demonstrates that our tracker achieves superior performance with a real-time
processing speed of 78 frames-per-second (fps).

本文提出了一种基于四元组深度网络的新方法以应对样本数据的潜在关联性，该方法在视觉物体跟踪中的性能优于其他模型并具有较快的处理速度。