• Corpus ID: 238634169

Trivial or impossible - dichotomous data difficulty masks model differences (on ImageNet and beyond)

@article{Meding2021TrivialOI,
  title={Trivial or impossible - dichotomous data difficulty masks model differences (on ImageNet and beyond)},
  author={Kristof Meding and Luca M. Schulze Buschoff and Robert Geirhos and Felix Wichmann},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.05922}
}
“The power of a generalization system follows directly from its biases” (Mitchell Today, CNNs are incredibly powerful generalisation systems—but to what degree have we understood how their inductive bias influences model decisions? We here attempt to disentangle the various aspects that determine how a model decides. In particular, we ask: what makes one model decide differently from another? In a meticulously controlled setting, we find that (1.) irrespective of the network architecture or… 
A Tour of Visualization Techniques for Computer Vision Datasets
TLDR
A number of data visualization techniques for analyzing Computer Vision (CV) datasets are surveyed to help understand properties and latent patterns in such data, by applying dataset-level analysis.
Partial success in closing the gap between human and machine vision
TLDR
The longstanding distortion robustness gap between humans and CNNs is closing, with the best models now exceeding human feedforward performance on most of the investigated OOD datasets, and the behavioural difference between human and machine vision is narrowing.

References

SHOWING 1-10 OF 77 REFERENCES
Partial success in closing the gap between human and machine vision
TLDR
The longstanding distortion robustness gap between humans and CNNs is closing, with the best models now exceeding human feedforward performance on most of the investigated OOD datasets, and the behavioural difference between human and machine vision is narrowing.
Do Adversarially Robust ImageNet Models Transfer Better?
TLDR
It is found that adversarially robust models, while less accurate, often perform better than their standard-trained counterparts when used for transfer learning, and this work focuses on adversARially robust ImageNet classifiers.
Learning Multiple Layers of Features from Tiny Images
TLDR
It is shown how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex, using a novel parallelization algorithm to distribute the work among multiple machines connected on a network.
Confident Learning: Estimating Uncertainty in Dataset Labels
TLDR
This work combines building on the assumption of a classification noise process to directly estimate the joint distribution between noisy (given) labels and uncorrupted (unknown) labels, resulting in a generalized CL which is provably consistent and experimentally performant.
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
TLDR
Surprisingly, it is found that lower capacity models may be practically more useful than higher capacity models in real-world datasets with high proportions of erroneously labeled data.
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency
A central problem in cognitive science and behavioural neuroscience as well as in machine learning and artificial intelligence research is to ascertain whether two or more decision makers (e.g.
On the surprising similarities between supervised and self-supervised models
TLDR
Surprisingly, current self-supervised CNNs share four key characteristics of their supervised counterparts: relatively poor noise robustness,Non-human category-level error patterns, non-human image-levelerror patterns, high similarity to supervised model errors and a bias towards texture.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
TLDR
Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.
Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training
TLDR
This paper conducts the first empirical study to quantify the impact of software implementation on the fairness and its variance of DL systems and calls for better fairness evaluation and testing protocols to improve fairness and fairness variance ofDL systems as well as DL research validity and reproducibility at large.
Deep High-Resolution Representation Learning for Visual Recognition
TLDR
The superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, is shown, suggesting that the HRNet is a stronger backbone for computer vision problems.
...
1
2
3
4
5
...