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Found 14 papers, 13 papers with code
Self-supervised learning of video representations from a child's perspective
These results suggest that important temporal aspects of a child's internal model of the world may be learnable from their visual experience using highly generic learning algorithms and without strong inductive biases.
Scaling may be all you need for achieving human-level object recognition capacity with human-like visual experience
We find that it is feasible to reach human-level object recognition capacity at sub-human scales of model size, data size, and image size, if these factors are scaled up simultaneously.
Learning high-level visual representations from a child's perspective without strong inductive biases
Young children develop sophisticated internal models of the world based on their visual experience.
Recognition, recall, and retention of few-shot memories in large language models
In recognition experiments, we ask if the model can distinguish the seen example from a novel example; in recall experiments, we ask if the model can correctly recall the seen example when cued by a part of it; and in retention experiments, we periodically probe the model's memory for the original examples as the model is trained continuously with new examples.
Can deep learning match the efficiency of human visual long-term memory in storing object details?
Humans have a remarkably large capacity to store detailed visual information in long-term memory even after a single exposure, as demonstrated by classic experiments in psychology.
Compositional generalization in semantic parsing with pretrained transformers
Finally, we show that larger models are harder to train from scratch and their generalization accuracy is lower when trained up to convergence on the relatively small SCAN and COGS datasets, but the benefits of large-scale pretraining become much clearer with larger models.
How much human-like visual experience do current self-supervised learning algorithms need in order to achieve human-level object recognition?
The exact values of these estimates are sensitive to some underlying assumptions, however even in the most optimistic scenarios they remain orders of magnitude larger than a human lifetime.
Self-supervised learning through the eyes of a child
Within months of birth, children develop meaningful expectations about the world around them.
Robustness properties of Facebook's ResNeXt WSL models
We show that these models display an unprecedented degree of robustness against common image corruptions and perturbations, as measured by the ImageNet-C and ImageNet-P benchmarks.
Improving the robustness of ImageNet classifiers using elements of human visual cognition
As reported in previous work, we show that an explicit episodic memory improves the robustness of image recognition models against small-norm adversarial perturbations under some threat models.
Improved memory in recurrent neural networks with sequential non-normal dynamics
In the presence of a non-linearity, orthogonal transformations no longer preserve norms, suggesting that alternative transformations might be better suited to non-linear networks.
A Simple Cache Model for Image Recognition
We propose to extract this extra class-relevant information using a simple key-value cache memory to improve the classification performance of the model at test time.
Skip Connections Eliminate Singularities
Here, we present a novel explanation for the benefits of skip connections in training very deep networks.
Efficient Probabilistic Inference in Generic Neural Networks Trained with Non-Probabilistic Feedback
We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks.
