We address the problem of learning self-supervised representations from
unlabeled image collections. Unlike existing approaches that attempt to learn
useful features by maximizing similarity between augmented versions of each
input image or by speculatively picking negative samples, we instead also make
use of the natural variation that occurs in image collections that are captured
using static monitoring cameras. To achieve this, we exploit readily available
context data that encodes information such as the spatial and temporal
relationships between the input images. We are able to learn representations
that are surprisingly effective for downstream supervised classification, by
first identifying high probability positive pairs at training time, i.e. those
images that are likely to depict the same visual concept. For the critical task
of global biodiversity monitoring, this results in image features that can be
adapted to challenging visual species classification tasks with limited human
supervision. We present results on four different camera trap image
collections, across three different families of self-supervised learning
methods, and show that careful image selection at training time results in
superior performance compared to existing baselines such as conventional
self-supervised training and transfer learning.

利用静态监控相机捕获的图像集合中的自然变化和上下文数据来学习无标签图像集合的自我监督表示，通过在训练时识别高概率正面对，即可能描述相同视觉概念的图像，可以获得出色的下游超级分类性能。

聚焦正面：自监督学习用于生物多样性监测

Focus on the Positives: Self-Supervised Learning for Biodiversity  Monitoring

Existing benchmarks used to evaluate the performance of end-to-end neural
dialog systems lack a key component: natural variation present in human
conversations. Most datasets are constructed through crowdsourcing, where the
crowd workers follow a fixed template of instructions while enacting the role
of a user/agent. This results in straight-forward, somewhat routine, and mostly
trouble-free conversations, as crowd workers do not think to represent the full
range of actions that occur naturally with real users. In this work, we
investigate the impact of naturalistic variation on two goal-oriented datasets:
bAbI dialog task and Stanford Multi-Domain Dataset (SMD). We also propose new
and more effective testbeds for both datasets, by introducing naturalistic
variation by the user. We observe that there is a significant drop in
performance (more than 60% in Ent. F1 on SMD and 85% in per-dialog accuracy on
bAbI task) of recent state-of-the-art end-to-end neural methods such as BossNet
and GLMP on both datasets.

探究自然变化对神经对话系统的影响，提出了两个考虑用户自然变化的新数据集，发现 BossNet 和 GLMP 等最新端到端神经方法在这些数据集上的表现严重下降。

目标导向对话中自然变化的影响

Effects of Naturalistic Variation in Goal-Oriented Dialog

While deep learning has resulted in major breakthroughs in many application
domains, the frameworks commonly used in deep learning remain fragile to
artificially-crafted and imperceptible changes in the data. In response to this
fragility, adversarial training has emerged as a principled approach for
enhancing the robustness of deep learning with respect to norm-bounded
perturbations. However, there are other sources of fragility for deep learning
that are arguably more common and less thoroughly studied. Indeed, natural
variation such as lighting or weather conditions can significantly degrade the
accuracy of trained neural networks, proving that such natural variation
presents a significant challenge for deep learning.
In this paper, we propose a paradigm shift from perturbation-based
adversarial robustness toward model-based robust deep learning. Our objective
is to provide general training algorithms that can be used to train deep neural
networks to be robust against natural variation in data. Critical to our
paradigm is first obtaining a model of natural variation which can be used to
vary data over a range of natural conditions. Such models may be either known a
priori or else learned from data. In the latter case, we show that deep
generative models can be used to learn models of natural variation that are
consistent with realistic conditions. We then exploit such models in three
novel model-based robust training algorithms in order to enhance the robustness
of deep learning with respect to the given model. Our extensive experiments
show that across a variety of naturally-occurring conditions and across various
datasets, deep neural networks trained with our model-based algorithms
significantly outperform both standard deep learning algorithms as well as
norm-bounded robust deep learning algorithms.

文章提出了从基于扰动的对抗鲁棒性转向基于模型的鲁棒深度学习的新范式，并探讨了使用深度生成模型来学习自然变化模型并进一步提高深度学习对于自然变化的鲁棒性的三种新型算法，实验表明，该方法在自然情况下能够超越标准深度学习算法和基于范数的鲁棒深度学习算法。