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.

Input: Consider Input: Neural networks offer high-accuracy solutions to a range of problems, but are computationally costly to run in production systems. We propose a technique called Deep Learning Approximation to take an already-trained neural network model and build a faster (and almost equally accurate) network by manipulating the network structure and coefficients without requiring re-training or access to the training data. Speedup is achieved by applying a sequential series of independent optimizations that reduce the floating-point operations (FLOPs) required to perform a forward pass. An optimal lossy approximation is chosen for each layer by weighing the relative accuracy loss and FLOP reduction. On PASCAL VOC 2007 with the YOLO network, we show an end-to-end 2x speedup in a network forward pass with a $5$\% drop in mAP that can be re-gained by finetuning, enabling this network (and others like it) to be deployed in compute-constrained systems.

Output: Decompose weights to use fewer FLOPs with SVD

Input: Consider Input: Federated learning, where a global model is trained by iterative parameter averaging of locally-computed updates, is a promising approach for distributed training of deep networks; it provides high communication-efficiency and privacy-preservability, which allows to fit well into decentralized data environments, e.g., mobile-cloud ecosystems. However, despite the advantages, the federated learning-based methods still have a challenge in dealing with non-IID training data of local devices (i.e., learners). In this regard, we study the effects of a variety of hyperparametric conditions under the non-IID environments, to answer important concerns in practical implementations: (i) We first investigate parameter divergence of local updates to explain performance degradation from non-IID data. The origin of the parameter divergence is also found both empirically and theoretically. (ii) We then revisit the effects of optimizers, network depth/width, and regularization techniques; our observations show that the well-known advantages of the hyperparameter optimization strategies could rather yield diminishing returns with non-IID data. (iii) We finally provide the reasons of the failure cases in a categorized way, mainly based on metrics of the parameter divergence.

Output: We investigate the internal reasons of our observations, the diminishing effects of the well-known hyperparameter optimization methods on federated learning from decentralized non-IID data.

Input: Consider Input: Manipulation and re-use of images in scientific publications is a recurring problem, at present lacking a scalable solution. Existing tools for detecting image duplication are mostly manual or semi-automated, despite the fact that generating data for a learning-based approach is straightforward, as we here illustrate. This paper addresses the problem of determining if, given two images, one is a manipulated version of the other by means of certain geometric and statistical manipulations, e.g. copy, rotation, translation, scale, perspective transform, histogram adjustment, partial erasing, and compression artifacts. We propose a solution based on a 3-branch Siamese Convolutional Neural Network. The ConvNet model is trained to map images into a 128-dimensional space, where the Euclidean distance between duplicate (respectively, unique) images is no greater (respectively, greater) than 1. Our results suggest that such an approach can serve as tool to improve surveillance of the published and in-peer-review literature for image manipulation. We also show that as a byproduct the network learns useful representations for semantic segmentation, with performance comparable to that of domain-specific models.
Output: A forensic metric to determine if a given image is a copy (with possible manipulation) of another image from a given dataset.