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Found 8 papers, 2 papers with code
MindSet: Vision. A toolbox for testing DNNs on key psychological experiments
Multiple benchmarks have been developed to assess the alignment between deep neural networks (DNNs) and human vision.
Convolutional Neural Networks Trained to Identify Words Provide a Surprisingly Good Account of Visual Form Priming Effects
A wide variety of orthographic coding schemes and models of visual word identification have been developed to account for masked priming data that provide a measure of orthographic similarity between letter strings.
Mixed Evidence for Gestalt Grouping in Deep Neural Networks
Here we test a total of 16 networks covering a variety of architectures and learning paradigms (convolutional, attention-based, supervised and self-supervised, feed-forward and recurrent) on dots (Experiment 1) and more complex shapes (Experiment 2) stimuli that produce strong Gestalts effects in humans.
Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be
When seeing a new object, humans can immediately recognize it across different retinal locations: the internal object representation is invariant to translation.
Learning Online Visual Invariances for Novel Objects via Supervised and Self-Supervised Training
Through the analysis of models' internal representations, we show that standard supervised CNNs trained on transformed objects can acquire strong invariances on novel classes even when trained with as few as 50 objects taken from 10 classes.
A case for robust translation tolerance in humans and CNNs. A commentary on Han et al
Han et al. (2020) reported a behavioral experiment that assessed the extent to which the human visual system can identify novel images at unseen retinal locations (what the authors call "intrinsic translation invariance") and developed a novel convolutional neural network model (an Eccentricity Dependent Network or ENN) to capture key aspects of the behavioral results.
Learning Translation Invariance in CNNs
In this work we show how, even though CNNs are not 'architecturally invariant' to translation, they can indeed 'learn' to be invariant to translation.
The human visual system and CNNs can both support robust online translation tolerance following extreme displacements
Although translation is perhaps the simplest spatial transform that the visual system needs to cope with, the extent to which the human visual system can identify objects at previously unseen locations is unclear, with some studies reporting near complete invariance over 10{\deg} and other reporting zero invariance at 4{\deg} of visual angle.
