Self-supervised learning (SSL) aims to produce useful feature representations without access to any human-labeled data annotations. Due to the success of recent SSL methods based on contrastive learning, such as SimCLR, this problem has gained popularity. Most current contrastive learning approaches append a parametrized projection head to the end of some backbone network to optimize the InfoNCE objective and then discard the learned projection head after training. This raises a fundamental question: Why is a learnable projection head required if we are to discard it after training? In this work, we first perform a systematic study on the behavior of SSL training focusing on the role of the projection head layers. By formulating the projection head as a parametric component for the InfoNCE objective rather than a part of the network, we present an alternative optimization scheme for training contrastive learning based SSL frameworks. Our experimental study on multiple image classification datasets demonstrates the effectiveness of the proposed approach over alternatives in the SSL literature.

在在下游决策取决于预测概率的安全关键应用中，校准神经网络是最重要的。测量校准误差相当于比较两个实证分布。在这项工作中，我们引入了由经典Kolmogorov-Smirnov（KS）统计测试的自由校准措施，其中主要思想是比较各自的累积概率分布。由此，通过通过Quidsime使用可微分函数来近似经验累积分布，我们获得重新校准函数，将网络输出映射到实际（校准的）类分配概率。使用停滞校准组进行脊柱拟合，并在看不见的测试集上评估所获得的重新校准功能。我们测试了我们对各种图像分类数据集的现有校准方法的方法，并且我们的样条键的重新校准方法始终如一地优于KS错误的现有方法以及其他常用的校准措施。我们的代码可在https://github.com/kartikgupta-at-anu/spline-calibration获得。

消失和爆炸渐变的问题是一种长期障碍，阻碍了神经网络的有效培训。尽管有各种诀窍和技巧，但在实践中被采用缓解问题，仍然缺乏令人满意的理论或可证明的解决方案。在本文中，我们从高维概率理论的角度解决了问题。我们提供了一个严谨的结果，如有轻微条件，如果神经网络具有足够的宽度，消失/爆炸梯度问题如何消失。我们的主要思想是通过新的激活函数，即高斯 - Poincar的归一化功能和正交权重矩阵来限制非线性神经网络中的前向和向后信号传播。综合性和现实世界数据的实验验证了我们的理论，并在实践中施用时确认了对非常深刻的神经网络的有效性。

由于在量化网络上的按位操作产生的有效存储器消耗和更快的计算，神经网络量化已经变得越来越受欢迎。尽管它们表现出优异的泛化能力，但其鲁棒性属性并不是很好地理解。在这项工作中，我们系统地研究量化网络对基于梯度的对抗性攻击的鲁棒性，并证明这些量化模型遭受梯度消失问题并显示出虚假的鲁棒感。通过归因于培训的网络中的渐变消失到较差的前后信号传播，我们引入了一个简单的温度缩放方法来缓解此问题，同时保留决策边界。尽管对基于梯度的对抗攻击进行了简单的修改，但具有多个网络架构的多个图像分类数据集的实验表明，我们的温度缩放攻击在量化网络上获得了近乎完美的成功率，同时表现出对普遍培训的模型以及浮动的原始攻击以及浮动 - 点网络。代码可在https://github.com/kartikgupta-at-anu/Attack-bnn获得。

Pruning large neural networks while maintaining their performance is often desirable due to the reduced space and time complexity. In existing methods, pruning is done within an iterative optimization procedure with either heuristically designed pruning schedules or additional hyperparameters, undermining their utility. In this work, we present a new approach that prunes a given network once at initialization prior to training. To achieve this, we introduce a saliency criterion based on connection sensitivity that identifies structurally important connections in the network for the given task. This eliminates the need for both pretraining and the complex pruning schedule while making it robust to architecture variations. After pruning, the sparse network is trained in the standard way. Our method obtains extremely sparse networks with virtually the same accuracy as the reference network on the MNIST, CIFAR-10, and Tiny-ImageNet classification tasks and is broadly applicable to various architectures including convolutional, residual and recurrent networks. Unlike existing methods, our approach enables us to demonstrate that the retained connections are indeed relevant to the given task.

Incremental learning (IL) has received a lot of attention recently, however, the literature lacks a precise problem definition, proper evaluation settings, and metrics tailored specifically for the IL problem. One of the main objectives of this work is to fill these gaps so as to provide a common ground for better understanding of IL. The main challenge for an IL algorithm is to update the classifier whilst preserving existing knowledge. We observe that, in addition to forgetting, a known issue while preserving knowledge, IL also suffers from a problem we call intransigence, inability of a model to update its knowledge. We introduce two metrics to quantify forgetting and intransigence that allow us to understand, analyse, and gain better insights into the behaviour of IL algorithms. We present RWalk, a generalization of EWC++ (our efficient version of EWC [7]) and Path Integral [26] with a theoretically grounded KL-divergence based perspective. We provide a thorough analysis of various IL algorithms on MNIST and CIFAR-100 datasets. In these experiments, RWalk obtains superior results in terms of accuracy, and also provides a better trade-off between forgetting and intransigence.