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Found 18 papers, 9 papers with code
Immediate generalisation in humans but a generalisation lag in deep neural networks -- evidence for representational divergence?
Recent research has seen many behavioral comparisons between humans and deep neural networks (DNNs) in the domain of image classification.
Neither hype nor gloom do DNNs justice
Neither the hype exemplified in some exaggerated claims about deep neural networks (DNNs), nor the gloom expressed by Bowers et al. do DNNs as models in vision science justice: DNNs rapidly evolve, and today's limitations are often tomorrow's successes.
Are Deep Neural Networks Adequate Behavioural Models of Human Visual Perception?
Deep neural networks (DNNs) are machine learning algorithms that have revolutionised computer vision due to their remarkable successes in tasks like object classification and segmentation.
The developmental trajectory of object recognition robustness: children are like small adults but unlike big deep neural networks
Unlike adults', whose object recognition performance is robust against a wide range of image distortions, DNNs trained on standard ImageNet (1. 3M images) perform poorly on distorted images.
Trivial or impossible -- dichotomous data difficulty masks model differences (on ImageNet and beyond)
We find that the ImageNet validation set, among others, suffers from dichotomous data difficulty (DDD): For the range of investigated models and their accuracies, it is dominated by 46. 0% "trivial" and 11. 5% "impossible" images (beyond label errors).
Out-of-distribution robustness: Limited image exposure of a four-year-old is enough to outperform ResNet-50
Recent gains in model robustness towards out-of-distribution images are predominantly achieved through ever-increasing large-scale datasets.
Trivial or Impossible --- dichotomous data difficulty masks model differences (on ImageNet and beyond)
We find that the ImageNet validation set, among others, suffers from dichotomous data difficulty (DDD): For the range of investigated models and their accuracies, it is dominated by 46. 0% ``trivial'' and 11. 5% ``impossible'' images (beyond label errors).
ImageNet suffers from dichotomous data difficulty
We find that the ImageNet validation set suffers from dichotomous data difficulty (DDD): For the range of investigated models and their accuracies, it is dominated by 46. 3% "trivial" and 11. 3% "impossible" images.
Partial success in closing the gap between human and machine vision
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.
Deep Neural Models for color discrimination and color constancy
High levels of color constancy were achieved with different DNN architectures.
Psychophysical Estimation of Early and Late Noise
Moreover, it suggests that the use of external noise in the experiments may be helpful as an extra reference.
On the surprising similarities between supervised and self-supervised models
In the light of this recent breakthrough, we here compare self-supervised networks to supervised models and human behaviour.
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency
Here we introduce trial-by-trial error consistency, a quantitative analysis for measuring whether two decision making systems systematically make errors on the same inputs.
Shortcut Learning in Deep Neural Networks
Deep learning has triggered the current rise of artificial intelligence and is the workhorse of today's machine intelligence.
Perceiving the arrow of time in autoregressive motion
We employ a so-called frozen noise paradigm enabling us to compare human performance with four different algorithms on a trial-by-trial basis: A causal inference algorithm exploiting the dependence structure of additive noise terms, a neurally inspired network, a Bayesian ideal observer model as well as a simple heuristic.
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
Generalisation in humans and deep neural networks
We compare the robustness of humans and current convolutional deep neural networks (DNNs) on object recognition under twelve different types of image degradations.
Comparing deep neural networks against humans: object recognition when the signal gets weaker
In addition, we find progressively diverging classification error-patterns between humans and DNNs when the signal gets weaker, indicating that there may still be marked differences in the way humans and current DNNs perform visual object recognition.
