Deflating Dataset Bias Using Synthetic Data Augmentation

Jaipuria, Nikita; Zhang, Xianling; Bhasin, Rohan; Arafa, M.; Chakravarty, Punarjay; Shrivastava, Shubham; Manglani, Sagar; Murali, Vidya N.

doi:10.1109/cvprw50498.2020.00394

Cited by 47 publications

(21 citation statements)

References 23 publications

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“…Therefore, we can think of X l ∪ X l as a mixing of data, where X l acts as a regularising factor during training, which aims to prevent φ to become an irrecoverably bad detector. Thinking in a virtual-to-real UDA setting, we note that from traditional ML algorithms to modern deep CNNs, mixing virtual and real data has been shown to be systematically beneficial across different computer vision tasks [17,32,36,41]. Finally, we can see that to run the stopping criterion we rely on the full self-labeled data available at the beginning and end of each cycle, which results from applying the last available version of φ to the full X u set.…”

Section: Self-trainingmentioning

confidence: 99%

Co-training for On-board Deep Object Detection

Villalonga,

Lopez

2020

Preprint

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Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e. objects) within the training images. Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this paper, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation.

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Section: Self-trainingmentioning

confidence: 99%

Co-training for On-board Deep Object Detection

Villalonga,

Lopez

2020

Preprint

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“…Jaipuria et al [36] propose a bias mitigation approach by using aimed synthetic data augmentation that combines the advantages of gaming engine simulations and sim2real style transfer techniques to bridge the gaps in real data sets for vision tasks. However, instead of blindly collecting more data or mixing data sets that often ends up in worse final performance, they suggest a smarter approach to augment data regarding the task-specific noise factors.…”

Section: Expanding the Scope To Other Data Typesmentioning

confidence: 99%

Representation Bias in Data: A Survey on Identification and Resolution Techniques

Shahbazi¹,

Li²,

Asudeh³

et al. 2022

Preprint

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The grand goal of data-driven decision-making is to help humans make decisions, not only easily and at scale but also wisely, accurately, and just. However, data-driven algorithms are only as good as the data they work with, while data sets, especially social data, often miss representing minorities. Representation Bias in data can happen due to various reasons ranging from historical discrimination to selection and sampling biases in the data acquisition and preparation methods. One cannot expect AI-based societal solutions to have equitable outcomes without addressing the representation bias. This paper surveys the existing literature on representation bias in the data. It presents a taxonomy to categorize the studied techniques based on multiple design dimensions and provide a side-by-side comparison of their properties. There is still a long way to fully address representation bias issues in data. The authors hope that this survey motivates researchers to approach these challenges in the future by observing existing work within their respective domains.

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“…Differentiable rendering Algorithms from the computer graphics community can have intriguing implications for ML, for instance for the generation of synthetic training data for computer vision [677,678,679] and the class of methods referred to as "vision as inverse graphics" [680]. Rendering in computer graphics is the process of generating images of 3D scenes defined by geometry, textures, materials, lighting, and the properties and positions of one or more cameras.…”

Section: Honorable Mention Motifsmentioning

confidence: 99%

Simulation Intelligence: Towards a New Generation of Scientific Methods

Lavin¹,

Zenil²,

Krakauer³

et al. 2021

Preprint

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The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. 1 We present the Nine Motifs of Simulation Intelligence, a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence "operating system" stack (SI-stack) and the motifs therein:1. Multi-physics and multi-scale modeling 2. Surrogate modeling and emulation 3. Simulation-based inference 4. Causal modeling and inference 5. Agent-based modeling 6. Probabilistic programming 7. Differentiable programming 8. Open-ended optimization Machine programmingWe believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science.

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Deflating Dataset Bias Using Synthetic Data Augmentation

Cited by 47 publications

References 23 publications

Co-training for On-board Deep Object Detection

Co-training for On-board Deep Object Detection

Representation Bias in Data: A Survey on Identification and Resolution Techniques

Simulation Intelligence: Towards a New Generation of Scientific Methods

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