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Found 19 papers, 6 papers with code
The 3D-PC: a benchmark for visual perspective taking in humans and machines
It is an essential feature of human intelligence, which develops over the first decade of life and requires an ability to process the 3D structure of visual scenes.
Neural scaling laws for phenotypic drug discovery
Recent breakthroughs by deep neural networks (DNNs) in natural language processing (NLP) and computer vision have been driven by a scale-up of models and data rather than the discovery of novel computing paradigms.
Fixing the problems of deep neural networks will require better training data and learning algorithms
Bowers and colleagues argue that DNNs are poor models of biological vision because they often learn to rival human accuracy by relying on strategies that differ markedly from those of humans.
Diagnosing and exploiting the computational demands of videos games for deep reinforcement learning
Humans learn by interacting with their environments and perceiving the outcomes of their actions.
Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization
However, its widespread adoption has been limited due to a reliance on tricks to generate interpretable images, and corresponding challenges in scaling it to deeper neural networks.
Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception
Harmonized DNNs achieve the best of both worlds and experience attacks that are detectable and affect features that humans find diagnostic for recognition, meaning that attacks on these models are more likely to be rendered ineffective by inducing similar effects on human perception.
Harmonizing the object recognition strategies of deep neural networks with humans
Across 84 different DNNs trained on ImageNet and three independent datasets measuring the where and the how of human visual strategies for object recognition on those images, we find a systematic trade-off between DNN categorization accuracy and alignment with human visual strategies for object recognition.
The Challenge of Appearance-Free Object Tracking with Feedforward Neural Networks
Here, we introduce $\textit{PathTracker}$, a visual challenge inspired by cognitive psychology, which tests the ability of observers to learn to track objects solely by their motion.
Understanding the computational demands underlying visual reasoning
Our analysis reveals a novel taxonomy of visual reasoning tasks, which can be primarily explained by both the type of relations (same-different vs. spatial-relation judgments) and the number of relations used to compose the underlying rules.
Tracking Without Re-recognition in Humans and Machines
For this, we introduce PathTracker, a synthetic visual challenge that asks human observers and machines to track a target object in the midst of identical-looking "distractor" objects.
Recurrent neural circuits for contour detection
We introduce a deep recurrent neural network architecture that approximates visual cortical circuits.
Stable and expressive recurrent vision models
We posit that the effectiveness of recurrent vision models is bottlenecked by the standard algorithm used for training them, "back-propagation through time" (BPTT), which has O(N) memory-complexity for training an N step model.
Disentangling neural mechanisms for perceptual grouping
Forming perceptual groups and individuating objects in visual scenes is an essential step towards visual intelligence.
Learning what and where to attend with humans in the loop
Most recent gains in visual recognition have originated from the inclusion of attention mechanisms in deep convolutional networks (DCNs).
Learning long-range spatial dependencies with horizontal gated recurrent units
Progress in deep learning has spawned great successes in many engineering applications.
Robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks
We first demonstrate that current state-of-the-art approaches to neuron segmentation perform poorly on the challenge.
Learning what and where to attend
Most recent gains in visual recognition have originated from the inclusion of attention mechanisms in deep convolutional networks (DCNs).
Learning long-range spatial dependencies with horizontal gated-recurrent units
As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching -- and sometimes even surpassing -- human accuracy on a variety of visual recognition tasks.
What are the visual features underlying human versus machine vision?
Our study demonstrates that the narrowing gap between the object recognition accuracy of human observers and DCNs obscures distinct visual strategies used by each to achieve this performance.
