Topic: Momentum Contrast

The internal activations of particular deep neural networks (DNNs) are remarkably similar to the neuronal population responses along the ventral visual cortex in primates. Nevertheless, the similarities between the two are often investigated through stimulus sets consisting of everyday objects under naturalistic settings. Recent work has revealed a gap in generalization ability of these models in predicting neuronal responses to out-of-distribution (OOD) samples (i.e. samples that are not regarded as natural photos) . Here, we investigated how the recent progress in improving DNNs’ object re

Children learn to build a visual representation of the world from unsupervised exploration and we hypothesize that a key part of this learning ability is the use of self-generated navigational information as a similarity label to drive a learning objective for self-supervised learning. The goal of this work is to exploit navigational information in a visual environment to provide performance in training that exceeds the state-of-the-art self-supervised training. Here, we show that using spatial and temporal information in the pretraining stage of contrastive learning can improve the performan