Understanding Generative AI Content with Embedding Models

• URL: http://arxiv.org/abs/2408.10437v2
• Date: Thu, 22 Aug 2024 21:50:46 GMT
• Title: Understanding Generative AI Content with Embedding Models
• Authors: Max Vargas, Reilly Cannon, Andrew Engel, Anand D. Sarwate, Tony Chiang,
• Abstract summary: This work views the internal representations of modern deep neural networks (DNNs) as an automated form of traditional feature engineering. We show that these embeddings can reveal interpretable, high-level concepts in unstructured sample data. We find empirical evidence that there is inherent separability between real data and that generated from AI models.
• Score: 4.662332573448995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The construction of high-quality numerical features is critical to any quantitative data analysis. Feature engineering has been historically addressed by carefully hand-crafting data representations based on domain expertise. This work views the internal representations of modern deep neural networks (DNNs), called embeddings, as an automated form of traditional feature engineering. For trained DNNs, we show that these embeddings can reveal interpretable, high-level concepts in unstructured sample data. We use these embeddings in natural language and computer vision tasks to uncover both inherent heterogeneity in the underlying data and human-understandable explanations for it. In particular, we find empirical evidence that there is inherent separability between real data and that generated from AI models.

Related papers

• The Extrapolation Power of Implicit Models [2.3526338188342653]
  Implicit models are put to the test across various extrapolation scenarios: out-of-distribution, geographical, and temporal shifts. Our experiments consistently demonstrate significant performance advantage with implicit models.
  arXiv  Detail & Related papers  (2024-07-19T16:01:37Z)
• Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective [35.620874971064765]
  This work takes a "data-centric" view, examining how data collection, processing, and analysis contribute to explainable AI (XAI) We categorize existing work into three categories subject to their purposes: interpretations of deep models, influences of training data, and insights of domain knowledge. Specifically, we distill XAI methodologies into data mining operations on training and testing data across modalities.
  arXiv  Detail & Related papers  (2024-01-09T06:27:09Z)
• Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations [1.9580473532948401]
  This thesis explores the theoretical foundations of deep learning by studying the relationship between the architecture of these models and the inherent structures found within the data they process. We ask What drives the efficacy of deep learning algorithms and allows them to beat the so-called curse of dimensionality. Our methodology takes an empirical approach to deep learning, combining experimental studies with physics-inspired toy models.
  arXiv  Detail & Related papers  (2023-10-24T19:50:41Z)
• AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
  generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs. This paper explores the innovative concept of harnessing these AI-generated images as new data sources. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
  arXiv  Detail & Related papers  (2023-10-03T06:55:19Z)
• Homological Convolutional Neural Networks [4.615338063719135]
  We propose a novel deep learning architecture that exploits the data structural organization through topologically constrained network representations. We test our model on 18 benchmark datasets against 5 classic machine learning and 3 deep learning models.
  arXiv  Detail & Related papers  (2023-08-26T08:48:51Z)
• Persistence-based operators in machine learning [62.997667081978825]
  We introduce a class of persistence-based neural network layers. Persistence-based layers allow the users to easily inject knowledge about symmetries respected by the data, are equipped with learnable weights, and can be composed with state-of-the-art neural architectures.
  arXiv  Detail & Related papers  (2022-12-28T18:03:41Z)
• Experimental Observations of the Topology of Convolutional Neural Network Activations [2.4235626091331737]
  Topological data analysis provides compact, noise-robust representations of complex structures. Deep neural networks (DNNs) learn millions of parameters associated with a series of transformations defined by the model architecture. In this paper, we apply cutting edge techniques from TDA with the goal of gaining insight into the interpretability of convolutional neural networks used for image classification.
  arXiv  Detail & Related papers  (2022-12-01T02:05:44Z)
• TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments [84.6017003787244]
  This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets. We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
  arXiv  Detail & Related papers  (2022-08-16T20:46:08Z)
• Dynamic Inference with Neural Interpreters [72.90231306252007]
  We present Neural Interpreters, an architecture that factorizes inference in a self-attention network as a system of modules. inputs to the model are routed through a sequence of functions in a way that is end-to-end learned. We show that Neural Interpreters perform on par with the vision transformer using fewer parameters, while being transferrable to a new task in a sample efficient manner.
  arXiv  Detail & Related papers  (2021-10-12T23:22:45Z)
• Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation [51.29486247405601]
  We design a framework to generate counterfactuals for raw data instances with the proposed Attribute-Informed Perturbation (AIP) By utilizing generative models conditioned with different attributes, counterfactuals with desired labels can be obtained effectively and efficiently. Experimental results on real-world texts and images demonstrate the effectiveness, sample quality as well as efficiency of our designed framework.
  arXiv  Detail & Related papers  (2021-01-18T08:37:13Z)
• Relation-Guided Representation Learning [53.60351496449232]
  We propose a new representation learning method that explicitly models and leverages sample relations. Our framework well preserves the relations between samples. By seeking to embed samples into subspace, we show that our method can address the large-scale and out-of-sample problem.
  arXiv  Detail & Related papers  (2020-07-11T10:57:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.