Deep CNNs vs Ventral Stream in Image Recognition

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Questions and Answers

What is a characteristic of top layers of deeper CNNs?

Predicting IT neural responses at early phases

Implementing feedforward connections

Having fewer number of challenge images

Predicting IT neural responses at late phases (correct)

What is observed in deeper CNNs?

Challenge images with shorter OSTs in the IT cortex

No change in the number of challenge images

An increased number of challenge images

A reduced number of challenge images (correct)

What is a characteristic of challenge images for deeper CNNs?

Showing shorter OSTs in the IT cortex

Having no effect on OSTs in the IT cortex

Being solved by early phases of IT responses

Showing even longer OSTs in the IT cortex (correct)

What is CORnet?

A four-layered recurrent neural network model Signup and view all the answers

What is a characteristic of the top layer of CORnet?

Having within-area recurrent connections with shared weights Signup and view all the answers

What is a characteristic of pass 1 and pass 2 of CORnet?

Better predictors of early time bins Signup and view all the answers

What is a characteristic of late passes (especially pass 4) of CORnet?

Better predictors of late-phase IT responses Signup and view all the answers

What do the results of CORnet suggest?

Recurrent computations in the ventral stream Signup and view all the answers

What is a key advantage of deeper CNNs like inception-v3 and ResNet-50 over shallower networks like AlexNet?

They introduce more nonlinear transformations to the image pixels Signup and view all the answers

What is the function of recurrent computations in perception?

To enable recognition of partially visible objects Signup and view all the answers

What is the purpose of the study by Tang et al. (2018)?

To test the hypothesis that pattern completion is implemented by recurrent computations Signup and view all the answers

What happens when an image is rapidly followed by a spatially overlapping mask?

It interrupts any additional processing of the image. Signup and view all the answers

What is the minimum percentage of object visibility required for the visual system to make inferences?

10% Signup and view all the answers

What is the effect of backward masking on object recognition?

It disrupts recognition of partially visible objects Signup and view all the answers

What is a limitation of standard feed-forward models?

They are not robust to occlusion. Signup and view all the answers

What is the result of visual categorization of objects when only partial information is available?

Object recognition is robust to limited visibility Signup and view all the answers

What was observed in the performance of feed-forward models at limited visibility?

Their performance declined. Signup and view all the answers

What was found to be correlated with the latency of neural response?

The computational distance of each partial object to its whole object category mean. Signup and view all the answers

What is the difference between masked and unmasked stimuli in the study by Tang et al. (2018)?

The presence or absence of backward masking Signup and view all the answers

What type of networks can perform pattern completion?

Recurrent networks. Signup and view all the answers

What is the relationship between recurrent circuits in the primate brain and deep CNNs?

Deep CNNs are a partial approximation of recurrent circuits Signup and view all the answers

What was added to the AlexNet architecture to improve recognition of partially visible objects?

Recurrent connections to the fc7 layer. Signup and view all the answers

What was visualized using stochastic neighborhood embedding?

The temporal evolution of the feature representation for RNNh. Signup and view all the answers

What is a characteristic of attractor networks?

They can perform pattern completion. Signup and view all the answers

What was observed in the representation of whole objects and partial objects from different categories?

A clear separation between whole objects and partial objects from different categories Signup and view all the answers

What happened to the representation of partial objects over time in the clusters of whole images?

It approached the correct category Signup and view all the answers

What is the typical time frame for the RNNh model's performance and correlation with humans to saturate?

Around 10-20 time steps Signup and view all the answers

What is the physiological response to heavily occluded objects, which is consistent with the RNNh model?

Responses arising at around 200 ms Signup and view all the answers

What happened to the RNN model's performance when backward masking was introduced?

It was impaired and reduced Signup and view all the answers

What is a critical aspect of cognition, as mentioned in the text?

Making inferences from partial information Signup and view all the answers

What is a limitation of supervised DCNN models in explaining human visual cortex development?

They cannot learn from unlabeled data Signup and view all the answers

What is a key difference between human visual input and standard image databases?

Human input is multimodal, while image databases are unimodal Signup and view all the answers

How might humans augment their initial dataset during offline states?

By using already encountered instances to create new instances Signup and view all the answers

What is a potential role of unsupervised learning in visual cortex development?

To support the continuous adaptation of cortical sensory representations to sensory input statistics Signup and view all the answers

What is the Local Aggregation (LA) method used for?

To identify close neighbors and background neighbors in an embedded space Signup and view all the answers

Why are supervised DCNN models not suitable for explaining human visual cortex development?

Because they require a large amount of labeled data Signup and view all the answers

What is a difference between human learning and supervised DCNN models?

Humans can learn with unlabeled data, while DCNN models require labeled data Signup and view all the answers

What might be an inductive bias in human learning?

Objects obey the laws of physics and behave in a causally predictable way Signup and view all the answers

What is the purpose of the optimization process in the embedding space?

To push the current embedding vector closer to its close neighbors and further from its background neighbors Signup and view all the answers

What is the algorithm used to visualize the embedding space?

Multi-Dimensional Scaling (MDS) Signup and view all the answers

What is the main advantage of contrastive embedding methods?

They yield high-performing neural networks Signup and view all the answers

What is the dataset used for training the contrastive embedding models?

ImageNet Signup and view all the answers

What is the evaluation metric used to assess the transferability of the contrastive embedding models?

Object position and size estimation Signup and view all the answers

What is the finding of the study in terms of the contrastive embedding models' performance?

They equal or outperform category-supervised models in several tasks Signup and view all the answers

What is the architecture used for the contrastive embedding models?

ResNet18 Signup and view all the answers

What is the purpose of applying the MDS algorithm to the 600 images?

To visualize the embedding space Signup and view all the answers

What is the main difference between the ImageNet dataset and real biological data streams?

ImageNet presents objects from stereotypical angles, while biological data streams receive images from a much smaller set of object instances Signup and view all the answers

What is the name of the dataset that better represents the real infant data stream?

SAYCam Signup and view all the answers

Which area of the macaque brain did only a subset of unsupervised methods achieve parity with the supervised model in predicting neural responses?

V4 Signup and view all the answers

What is the main advantage of using deep contrastive learning on first-person video data from children?

It can learn from a much smaller set of object instances under noisy conditions Signup and view all the answers

Which area of the macaque brain did the best-performing contrastive embedding methods achieve neural prediction parity with supervised models?

IT Signup and view all the answers

What is a characteristic of the ImageNet dataset?

It contains single images of a large number of distinct instances of objects in each category Signup and view all the answers

What is the age range of the children in the SAYCam dataset?

6 to 32 months Signup and view all the answers

What is the duration of the video data in the SAYCam dataset?

About 2 hours/week Signup and view all the answers

What is the purpose of the VIE algorithm?

To test the robustness of contrastive unsupervised learning Signup and view all the answers

What is achieved by the VIE algorithm?

State-of-the-art results in dynamic visual tasks Signup and view all the answers

What is the main difference between semisupervised learning and purely unsupervised learning?

The use of labeled data in semisupervised learning Signup and view all the answers

How does the local label propagation (LLP) algorithm work?

It uses a label propagation method to infer the pseudolabels of unlabeled images from those of nearby labeled images Signup and view all the answers

What is the result of using semisupervised models with 3% supervision?

Representations that are substantially more behaviorally consistent than purely unsupervised methods Signup and view all the answers

What is the main advantage of semisupervised models over purely unsupervised models?

They lead to more behaviorally consistent representations Signup and view all the answers

What is the relationship between the performance of semisupervised models and the amount of supervision?

The performance of semisupervised models increases with the amount of supervision Signup and view all the answers

What is the main difference between semisupervised models and supervised models?

The amount of labeled data used Signup and view all the answers

Study Notes

Deeper CNNs and Ventral Stream

Deeper CNNs predict IT neural responses at late phases (150-250 ms) more accurately than 'regular-deep' models like AlexNet.
This suggests that deeper CNNs might be approximating 'unrolled' versions of the recurrent circuits of the ventral stream.
Deeper CNNs have a reduced number of challenge images, and the remaining challenge images show longer OSTs in the IT cortex.

CORnet Model

CORnet is a four-layered recurrent neural network model with within-area recurrent connections and shared weights.
The top layer of CORnet is comparable to IT and has higher IT predictivity for the late phase of IT responses.
Pass 1 and pass 2 of CORnet are better predictors of early time bins (relevant for control images), while late passes (especially pass 4) are better at predicting late phases of IT responses (crucial for challenge images).

Recurrent Computations

Recurrent computations act as additional nonlinear transformations of the initial feedforward during core object recognition.
Deeper CNNs, such as Inception-v3, v4, and ResNet-50, are better models of the behaviorally critical late phase of IT responses due to the introduction of more nonlinear transformations.

Image Completion and RNN

Recurrent computations enable pattern completion, which allows recognition of poorly visible or occluded objects.
The visual system can make inferences even when only 10-20% of the object is visible.

Backward Masking

Backward masking disrupts recognition of partially visible objects by interrupting any additional, presumably recurrent, processing of the image.

Feed-Forward Models and Occlusion

Standard feed-forward models, such as AlexNet, are not robust to occlusion and their performance declines at limited visibility.

RNN Models

Recurrent Neural Networks improve recognition of partially visible objects, with the RNNh model demonstrating a significant improvement over the standard AlexNet.
Attractor networks, such as the Hopfield network, can perform pattern completion.
The RNNh model's performance and correlation with humans saturate at around 10-20 time steps, consistent with the physiological responses to heavily occluded objects arising at around 200 ms.

Backward Masking and RNN Performance

Presenting a mask reduces RNN performance, reproducing the effect of backward masking on human performance.

Unsupervised Neural Networks

Unsupervised models are trained on ImageNet, a dataset of millions of category-labeled images, which is implausible for human infants and nonhuman primates
Supervised DCNN cannot explain how representations are learned in the brain
Unsupervised learning algorithms aim to learn representations from natural statistics without high-level labeling

Human Data vs. Standard Image Databases

Human data is continuous and egocentric, whereas standard image databases are not
Human input is multimodal, whereas model input is often unimodal
Humans may rely on different inductive biases, allowing for more data-efficient learning
Humans may enlarge their initial dataset by using already encountered instances to create new instances during offline states (i.e., imagination, dreaming)

Unsupervised Learning Algorithms

Local Aggregation (LA) method: optimizes to push the current embedding vector closer to its close neighbors and further from its background neighbors
Multi-dimensional scaling (MDS) algorithm: used to visualize the embedding space and shows classes with high and low validation accuracy

Contrastive Embedding Methods

Yield high-performing neural networks
Outperform other unsupervised methods and even category-supervised models in several tasks, including object position and size estimation
Equaled or outperformed category-supervised models in several tasks

Comparison to Neural Data from Macaque Cortex

Unsupervised neural network models were compared to neural data from macaque V1, V4, and IT cortex
Unsupervised methods were significantly better than the untrained baseline at predicting neural responses in Area V1
Only a subset of methods achieved parity with the supervised model in predictions of responses in Area V4
Only the best-performing contrastive embedding methods achieved neural prediction parity with supervised models in Area IT

Deep Contrastive Learning on First-Person Video Data from Children

ImageNet dataset diverges significantly from real biological data streams
SAYCam dataset is a better proxy of the real infant data stream, containing head-mounted video camera data from three children
Contrastive unsupervised learning is robust enough to handle real-world developmental video streams such as SAYCam
VIE algorithm is an extension of LA to video and achieves state-of-the-art results on a variety of dynamic visual tasks

Partial Supervision

Semisupervised learning leverages small numbers of labeled datapoints in the context of large amounts of unlabeled data
Local label propagation (LLP) algorithm embeds datapoints into a compact embedding space and infers pseudolabels of unlabeled images from those of nearby labeled images
LLP jointly optimizes to predict inferred pseudolabels while maintaining contrastive differentiation between embeddings with different pseudolabels
Semisupervised models lead to representations that are substantially more behaviorally consistent than purely unsupervised methods, although a gap to supervised models remains

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Description

This quiz explores the comparison between deeper CNNs and the ventral stream in image recognition, including their performance on challenge images and neural responses. It discusses the potential of deeper CNNs to approximate 'unrolled' versions of recurrent circuits.