Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions

• URL: http://arxiv.org/abs/2401.10217v2
• Date: Tue, 16 Jul 2024 03:04:34 GMT
• Title: Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions
• Authors: Namitha Padmanabhan, Matthew Gwilliam, Pulkit Kumar, Shishira R Maiya, Max Ehrlich, Abhinav Shrivastava,
• Abstract summary: Implicit Neural Representations (INRs) are neural networks trained as a continuous representation of a signal. Our work is a unified framework for explaining properties of INRs by examining the strength of each neuron's contribution to each output pixel.
• Score: 36.41141627989279
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The many variations of Implicit Neural Representations (INRs), where a neural network is trained as a continuous representation of a signal, have tremendous practical utility for downstream tasks including novel view synthesis, video compression, and image super-resolution. Unfortunately, the inner workings of these networks are seriously under-studied. Our work, eXplaining the Implicit Neural Canvas (XINC), is a unified framework for explaining properties of INRs by examining the strength of each neuron's contribution to each output pixel. We call the aggregate of these contribution maps the Implicit Neural Canvas and we use this concept to demonstrate that the INRs we study learn to "see" the frames they represent in surprising ways. For example, INRs tend to have highly distributed representations. While lacking high-level object semantics, they have a significant bias for color and edges, and are almost entirely space-agnostic. We arrive at our conclusions by examining how objects are represented across time in video INRs, using clustering to visualize similar neurons across layers and architectures, and show that this is dominated by motion. These insights demonstrate the general usefulness of our analysis framework. Our project page is available at https://namithap10.github.io/xinc.

Related papers

• Steinmetz Neural Networks for Complex-Valued Data [23.80312814400945]
  We introduce a new approach to processing complex-valued data using DNNs consisting of parallel real-valuedetzworks with coupled outputs. Our proposed class of architectures, referred to as Steinmetz Neural Networks, leverage multi-view learning to construct more interpretable representations within the latent space. Our numerical experiments depict the improved performance and to additive noise, afforded by these networks on benchmark datasets and synthetic examples.
  arXiv  Detail & Related papers  (2024-09-16T08:26:06Z)
• Extreme Compression of Adaptive Neural Images [6.646501936980895]
  Implicit Neural Representations (INRs) and Neural Fields are a novel paradigm for signal representation, from images and audio to 3D scenes and videos. We present a novel analysis on compressing neural fields, with the focus on images. We also introduce Adaptive Neural Images (ANI), an efficient neural representation that enables adaptation to different inference or transmission requirements.
  arXiv  Detail & Related papers  (2024-05-27T03:54:09Z)
• Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
  We propose to represent neural networks as computational graphs of parameters. Our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
  arXiv  Detail & Related papers  (2024-03-18T18:01:01Z)
• Signal Processing for Implicit Neural Representations [80.38097216996164]
  Implicit Neural Representations (INRs) encode continuous multi-media data via multi-layer perceptrons. Existing works manipulate such continuous representations via processing on their discretized instance. We propose an implicit neural signal processing network, dubbed INSP-Net, via differential operators on INR.
  arXiv  Detail & Related papers  (2022-10-17T06:29:07Z)
• 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)
• Neural Knitworks: Patched Neural Implicit Representation Networks [1.0470286407954037]
  We propose Knitwork, an architecture for neural implicit representation learning of natural images that achieves image synthesis. To the best of our knowledge, this is the first implementation of a coordinate-based patch tailored for synthesis tasks such as image inpainting, super-resolution, and denoising. The results show that modeling natural images using patches, rather than pixels, produces results of higher fidelity.
  arXiv  Detail & Related papers  (2021-09-29T13:10:46Z)
• Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans [56.63912568777483]
  This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views. We propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh. Experiments on ZJU-MoCap show that our approach outperforms prior works by a large margin in terms of novel view synthesis quality.
  arXiv  Detail & Related papers  (2020-12-31T18:55:38Z)
• Understanding the Role of Individual Units in a Deep Neural Network [85.23117441162772]
  We present an analytic framework to systematically identify hidden units within image classification and image generation networks. First, we analyze a convolutional neural network (CNN) trained on scene classification and discover units that match a diverse set of object concepts. Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes.
  arXiv  Detail & Related papers  (2020-09-10T17:59:10Z)
• A new approach to descriptors generation for image retrieval by analyzing activations of deep neural network layers [43.77224853200986]
  We consider the problem of descriptors construction for the task of content-based image retrieval using deep neural networks. It is known that the total number of neurons in the convolutional part of the network is large and the majority of them have little influence on the final classification decision. We propose a novel algorithm that allows us to extract the most significant neuron activations and utilize this information to construct effective descriptors.
  arXiv  Detail & Related papers  (2020-07-13T18:53:10Z)
• Locality Guided Neural Networks for Explainable Artificial Intelligence [12.435539489388708]
  We propose a novel algorithm for back propagation, called Locality Guided Neural Network(LGNN) LGNN preserves locality between neighbouring neurons within each layer of a deep network. In our experiments, we train various VGG and Wide ResNet (WRN) networks for image classification on CIFAR100.
  arXiv  Detail & Related papers  (2020-07-12T23:45:51Z)

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.