Zero-shot generalization across architectures for visual classification

• URL: http://arxiv.org/abs/2402.14095v4
• Date: Fri, 3 May 2024 15:25:09 GMT
• Title: Zero-shot generalization across architectures for visual classification
• Authors: Evan Gerritz, Luciano Dyballa, Steven W. Zucker,
• Abstract summary: Generalization to unseen data is a key desideratum for deep networks, but its relation to classification accuracy is unclear. We show that popular networks, from deep convolutional networks (CNNs) to transformers, vary in their power to extrapolate to unseen classes both across layers and across architectures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generalization to unseen data is a key desideratum for deep networks, but its relation to classification accuracy is unclear. Using a minimalist vision dataset and a measure of generalizability, we show that popular networks, from deep convolutional networks (CNNs) to transformers, vary in their power to extrapolate to unseen classes both across layers and across architectures. Accuracy is not a good predictor of generalizability, and generalization varies non-monotonically with layer depth.

Related papers

• Generalization emerges from local optimization in a self-organized learning network [0.0]
  We design and analyze a new paradigm for building supervised learning networks, driven only by local optimization rules without relying on a global error function. Our network stores new knowledge in the nodes accurately and instantaneously, in the form of a lookup table. We show on numerous examples of classification tasks that the networks generated by our algorithm systematically reach such a state of perfect generalization when the number of learned examples becomes sufficiently large. We report on the dynamics of the change of state and show that it is abrupt and has the distinctive characteristics of a first order phase transition, a phenomenon already observed for traditional learning networks and known as grokking.
  arXiv  Detail & Related papers  (2024-10-03T15:32:08Z)
• A separability-based approach to quantifying generalization: which layer is best? [0.0]
  Generalization to unseen data remains poorly understood for deep learning classification and foundation models. We provide a new method for evaluating the capacity of networks to represent a sampled domain. We find that (i) high classification accuracy does not imply high generalizability; and (ii) deeper layers in a model do not always generalize the best.
  arXiv  Detail & Related papers  (2024-05-02T17:54:35Z)
• Principled Architecture-aware Scaling of Hyperparameters [69.98414153320894]
  Training a high-quality deep neural network requires choosing suitable hyperparameters, which is a non-trivial and expensive process. In this work, we precisely characterize the dependence of initializations and maximal learning rates on the network architecture. We demonstrate that network rankings can be easily changed by better training networks in benchmarks.
  arXiv  Detail & Related papers  (2024-02-27T11:52:49Z)
• Rotation Equivariant Proximal Operator for Deep Unfolding Methods in Image Restoration [68.18203605110719]
  We propose a high-accuracy rotation equivariant proximal network that embeds rotation symmetry priors into the deep unfolding framework. This study makes efforts to suggest a high-accuracy rotation equivariant proximal network that effectively embeds rotation symmetry priors into the deep unfolding framework.
  arXiv  Detail & Related papers  (2023-12-25T11:53:06Z)
• Contrasting random and learned features in deep Bayesian linear regression [12.234742322758418]
  We study how the ability to learn affects the generalization performance of a simple class of models. By comparing deep random feature models to deep networks in which all layers are trained, we provide a detailed characterization of the interplay between width, depth, data density, and prior mismatch.
  arXiv  Detail & Related papers  (2022-03-01T15:51:29Z)
• Augmenting Convolutional networks with attention-based aggregation [55.97184767391253]
  We show how to augment any convolutional network with an attention-based global map to achieve non-local reasoning. We plug this learned aggregation layer with a simplistic patch-based convolutional network parametrized by 2 parameters (width and depth) It yields surprisingly competitive trade-offs between accuracy and complexity, in particular in terms of memory consumption.
  arXiv  Detail & Related papers  (2021-12-27T14:05:41Z)
• Impact of Aliasing on Generalization in Deep Convolutional Networks [29.41652467340308]
  We investigate the impact of aliasing on generalization in Deep Convolutional Networks. We show how to mitigate aliasing by inserting non-trainable low-pass filters at key locations.
  arXiv  Detail & Related papers  (2021-08-07T17:12:03Z)
• Redundant representations help generalization in wide neural networks [71.38860635025907]
  We study the last hidden layer representations of various state-of-the-art convolutional neural networks. We find that if the last hidden representation is wide enough, its neurons tend to split into groups that carry identical information, and differ from each other only by statistically independent noise.
  arXiv  Detail & Related papers  (2021-06-07T10:18:54Z)
• ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction [32.489371527159236]
  This work attempts to provide a plausible theoretical framework that aims to interpret modern deep (convolutional) networks from the principles of data compression and discriminative representation. We show that for high-dimensional multi-class data, the optimal linear discriminative representation maximizes the coding rate difference between the whole dataset and the average of all the subsets. We show that the basic iterative gradient ascent scheme for optimizing the rate reduction objective naturally leads to a multi-layer deep network, named ReduNet, that shares common characteristics of modern deep networks.
  arXiv  Detail & Related papers  (2021-05-21T16:29:57Z)
• Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior [80.5637175255349]
  We propose a new enriched prior based Dual-constrained Deep Semi-Supervised Coupled Factorization Network, called DS2CF-Net. To ex-tract hidden deep features, DS2CF-Net is modeled as a deep-structure and geometrical structure-constrained neural network. Our network can obtain state-of-the-art performance for representation learning and clustering.
  arXiv  Detail & Related papers  (2020-09-08T13:10:21Z)
• The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures [179.66117325866585]
  We investigate a design space that is usually overlooked, i.e. adjusting the channel configurations of predefined networks. We find that this adjustment can be achieved by shrinking widened baseline networks and leads to superior performance. Experiments are conducted on various networks and datasets for image classification, visual tracking and image restoration.
  arXiv  Detail & Related papers  (2020-06-29T17:59:26Z)

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.