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Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning

Adaptive Differential Filters for Fast and Communication-Eff...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Daniel Becking Heiner Kirchhoffer Gerhard Tech Paul Haase Karsten Mü ller Heiko Schwarz Wojciech Samek Fraunhofer Heinrich Hertz Institute (HHI) Berlin

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Federated learning (FL) scenarios inherently generate a large communication overhead by frequently transmitting neural network updates between clients and server. To minimize the communication cost, introducing sparsity in conjunction with differential updates is a commonly used technique. However, sparse model updates can slow down convergence speed or unintentionally skip certain update aspects, e.g., learned features, if error accumulation is not properly addressed. In this work, we propose a new scaling method operating at the granularity of convolutional filters which 1) compensates for highly sparse updates in FL processes, 2) adapts the local models to new data domains by enhancing some features in the filter space while diminishing others and 3) motivates extra sparsity in updates and thus achieves higher compression ratios, i.e., savings in the overall data transfer. Compared to unscaled updates and previous work, experimental results on different computer vision tasks (Pascal VOC, CIFAR10, Chest X-Ray) and neural networks (ResNets, MobileNets, VGGs) in uni-, bidirectional and partial update FL settings show that the proposed method improves the performance of the central server model while converging faster and reducing the total amount of transmitted data by up to 377×.

关键词： computer vision Adaptation models Computational modeling Pipelines Neural networks Collaborative work Data models

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Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation

Multi-Head Distillation for Continual Unsupervised Domain Ad...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Antoine Saporta Arthur Douillard Tuan-Hung Vu Patrick Pé rez Matthieu Cord Sorbonne Universit&#x00E9Valeo.ai Sorbonne Universit&#x00E9Heuritech Valeo.ai

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Unsupervised Domain Adaptation (UDA) is a transfer learning task which aims at training on an unlabeled target domain by leveraging a labeled source domain. Beyond the traditional scope of UDA with a single source domain and a single target domain, real-world perception systems face a variety of scenarios to handle, from varying lighting conditions to many cities around the world. In this context, UDAs with several domains increase the challenges with the addition of distribution shifts within the different target domains. This work focuses on a novel framework for learning UDA, continuous UDA, in which models operate on multiple target domains discovered sequentially, without access to previous target domains. We propose MuHDi, for Multi-Head Distillation, a method that solves the catastrophic forgetting problem, inherent in continual learning tasks. MuHDi performs distillation at multiple levels from the previous model as well as an auxiliary target-specialist segmentation head. We report both extensive ablation and experiments on challenging multi-target UDA semantic segmentation benchmarks to validate the proposed learning scheme and architecture.

关键词： Training Adaptation models Urban areas Semantics Transfer learning computer architecture Benchmark testing

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CPARR: Category-based Proposal Analysis for Referring Relationships

CPARR: Category-based Proposal Analysis for Referring Relati...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： He, Chuanzi Zhu, Haidong Gao, Jiyang Chen, Kan Nevatia, Ram Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9781728193601

The task of referring relationships is to localize subject and object entities in an image satisfying a relationship query, which is given in the form of . This requires simultaneous localization of the subject and object entities in a specified relationship. We introduce a simple yet effective proposal-based method for referring relationships. Different from the existing methods such as SSAS, our method can generate a high-resolution result while reducing its complexity and ambiguity. Our method is composed of two modules: a category-based proposal generation module to select the proposals related to the entities and a predicate analysis module to score the compatibility of pairs of selected proposals. We show state-of-the-art performance on the referring relationship task on two public datasets: Visual Relationship Detection and Visual Genome.

关键词： computer vision

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Leveraging combinatorial testing for safety-critical computer vision datasets

Leveraging combinatorial testing for safety-critical compute...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Gladisch, Christoph Heinzemann, Christian Herrmann, Martin Woehrle, Matthias Robert Bosch GmbH Corp Res Gerlingen Germany

ISBN: (纸本)9781728193601

Deep learning-based approaches have gained popularity for environment perception tasks such as semantic segmentation and object detection from images. However, the different nature of a data-driven deep neural nets (DNN) to conventional software is a challenge for practical software verification. In this work, we show how existing methods from software engineering provide benefits for the development of a DNN and in particular for dataset design and analysis. We show how combinatorial testing based on a domain model can be leveraged for generating test sets providing coverage guarantees with respect to important environmental features and their interaction. Additionally, we show how our approach can be used for growing a dataset, i.e. to identify where data is missing and should be collected next. We evaluate our approach on an internal use case and two public datasets.

关键词： Object detection

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SAM: The Sensitivity of Attribution Methods to Hyperparameters

SAM: The Sensitivity of Attribution Methods to Hyperparamete...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Bansal, Naman Agarwal, Chirag Anh Nguyen Auburn Univ Auburn AL 36849 USA Univ Illinois Chicago IL 60680 USA

ISBN: (纸本)9781728193601

Attribution methods can provide powerful insights into the reasons for a classifier's decision. We argue that a key desideratum of an explanation method is its robustness to input hyperparameters which are often randomly set or empirically tuned. High sensitivity to arbitrary hyperparameter choices does not only impede reproducibility but also questions the correctness of an explanation and impairs the trust of end-users. In this paper, we provide a thorough empirical study on the sensitivity of existing attribution methods. We found an alarming trend that many methods are highly sensitive to changes in their common hyperparameters e.g. even changing a random seed can yield a different explanation! Interestingly, such sensitivity is not reflected in the average explanation accuracy scores over the dataset as commonly reported in the literature. In addition, explanations generated for robust classifiers (i.e. which are trained to be invariant to pixel-wise perturbations) are surprisingly more robust than those generated for regular classifiers.

关键词： computer vision

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Hierarchical Image Classification using Entailment Cone Embeddings

Hierarchical Image Classification using Entailment Cone Embe...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Dhall, Ankit Makarova, Anastasia Ganea, Octavian Pavllo, Dario Greeff, Michael Krause, Andreas Swiss Fed Inst Technol Zurich Switzerland MIT Cambridge MA 02139 USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Image classification has been studied extensively, but there has been limited work in using unconventional, external guidance other than traditional image-label pairs for training. We present a set of methods for leveraging information about the semantic hierarchy embedded in class labels. We first inject label-hierarchy knowledge into an arbitrary CNN-based classifier and empirically show that availability of such external semantic information in conjunction with the visual semantics from images boosts overall performance. Taking a step further in this direction, we model more explicitly the label-label and label-image interactions using order-preserving embeddings governed by both Euclidean and hyperbolic geometries, prevalent in natural language, and tailor them to hierarchical image classification and representation learning. We empirically validate all the models on the hierarchical ETHEC dataset.

关键词： Visualization Semantics Task analysis Geometry Computational modeling Apertures computer vision

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Semi-Supervised Hyperspectral Object Detection Challenge Results - PBVS 2022

Semi-Supervised Hyperspectral Object Detection Challenge Res...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Aneesh Rangnekar Zachary Mulhollan Anthony Vodacek Matthew Hoffman Angel Sappa Erik Blasch Jun Yu Liwen Zhang Shenshen Du Hao Chang Keda Lu Zhong Zhang Fang Gao Ye Yu Feng Shuang Lei Wang Qiang Ling Pranjay Shyam Kuk-Jin Yoon Kyung-Soo Kim Rochester Institute of Technology Rochester NY USA ESPOL Polytechnic University Guayaquil Ecuador Computer Vision Center Campus UAB Barcelona Spain US Air Force Research Lab Rome NY

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop at the computer vision and pattern recognition (CVPR) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 × 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results.

关键词： Training computer vision conferences Training data Object detection Semisupervised learning Transformers

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Is Neuron Coverage Needed to Make Person Detection More Robust?

Is Neuron Coverage Needed to Make Person Detection More Robu...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Svetlana Pavlitskaya Ş iyar Yı kmı ş J. Marius Zö llner FZI Research Center for Information Technology Karlsruhe Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

The growing use of deep neural networks (DNNs) in safety- and security-critical areas like autonomous driving raises the need for their systematic testing. Coverage-guided testing (CGT) is an approach that applies mutation or fuzzing according to a predefined coverage metric to find inputs that cause misbehavior. With the introduction of a neuron coverage metric, CGT has also recently been applied to DNNs. In this work, we apply CGT to the task of person detection in crowded scenes. The proposed pipeline uses YOLOv3 for person detection and includes finding DNN bugs via sampling and mutation, and subsequent DNN retraining on the updated training set. To be a bug, we require a mutated image to cause a significant performance drop compared to a clean input. In accordance with the CGT, we also consider an additional requirement of increased coverage in the bug definition. In order to explore several types of robustness, our approach includes natural image transformations, corruptions, and adversarial examples generated with the Daedalus attack. The proposed framework has uncovered several thousand cases of incorrect DNN behavior. The relative change in mAP performance of the retrained models reached on average between 26.21% and 64.24% for different robustness types. However, we have found no evidence that the investigated coverage metrics can be advantageously used to improve robustness.

关键词： Measurement Deep learning computer vision computer bugs Neurons Neural networks Training data

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Where did I leave my keys? — Episodic-Memory-Based Question Answering on Egocentric Videos

Where did I leave my keys? — Episodic-Memory-Based Question...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Leonard Bä rmann Alex Waibel Interactive Systems Lab Karlsruhe Institute of Technology Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Humans have a remarkable ability to organize, compress and retrieve episodic memories throughout their daily life. Current AI systems, however, lack comparable capabilities as they are mostly constrained to an analysis with access to the raw input sequence, assuming an unlimited amount of data storage which is not feasible in realistic deployment scenarios. For instance, existing Video Question Answering (VideoQA) models typically reason over the video while already being aware of the question, thus requiring to store the complete video in case the question is not known in *** this paper, we address this challenge with three main contributions: First, we propose the Episodic Memory Question Answering (EMQA) task as a specialization of VideoQA. Specifically, EMQA models are constrained to keep only a constant-sized representation of the video input, thus automatically limiting the computation requirements at query time. Second, we introduce a new egocentric VideoQA dataset called QaEgo4D, far larger than existing egocentric VideoQA datasets and featuring video length unprecedented in VideoQA datasets in general. Third, we present extensive experiments on the new dataset, comparing various baseline models in both the VideoQA and the EMQA setting. To facilitate future research on egocentric VideoQA as well as episodic memory representation and retrieval, we publish our code and dataset.

关键词： computer vision Limiting Codes Computational modeling conferences Memory management Question answering (information retrieval)

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Continual Learning with Transformers for Image Classification

Continual Learning with Transformers for Image Classificatio...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Beyza Ermis Giovanni Zappella Martin Wistuba Aditya Rawal Cé dric Archambeau AWS Berlin AWS Santa Clara

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

In many real-world scenarios, data to train machine learning models become available over time. However, neural network models struggle to continually learn new concepts without forgetting what has been learnt in the past. This phenomenon is known as catastrophic forgetting and it is often difficult to prevent due to practical constraints, such as the amount of data that can be stored or the limited computation sources that can be used. Moreover, training large neural networks, such as Transformers, from scratch is very costly and requires a vast amount of training data, which might not be available in the application domain of interest. A recent trend indicates that dynamic architectures based on an expansion of the parameters can reduce catastrophic forgetting efficiently in continual learning, but this needs complex tuning to balance the growing number of parameters and barely share any information across tasks. As a result, they struggle to scale to a large number of tasks without significant overhead. In this paper, we validate in the computer vision domain a recent solution called Adaptive Distillation of Adapters (ADA), which is developed to perform continual learning using pre-trained Transformers and Adapters on text classification tasks. We empirically demonstrate on different classification tasks that this method maintains a good predictive performance without retraining the model or increasing the number of model parameters over the time. Besides it is significantly faster at inference time compared to the state-of-the-art methods.

关键词： Training computer vision Adaptation models Computational modeling Neural networks Training data Predictive models

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