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Inverting Visual Representations with Convolutional Networks
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Analyzing the Performance of Multilayer Neural Networks for Object Recognition
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Visualizing and Understanding Convolutional Networks
inferredcites →https://doi.org/10.1007/978-3-642-03737-5_49openalex-snapshot
Observer-Based Iterative Fuzzy and Neural Network Model Inversion for Measurement and Control Applications
inferredcites →https://doi.org/10.1016/j.cub.2011.08.031openalex-snapshot
Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies
inferredcites →https://doi.org/10.1023/b:visi.0000029664.99615.94openalex-snapshot
Distinctive Image Features from Scale-Invariant Keypoints
inferredcites →https://doi.org/10.1093/oso/9780198538493.001.0001openalex-snapshot
Neural Networks for Pattern Recognition
inferredcites →https://doi.org/10.1109/5.784232openalex-snapshot
Inversion of feedforward neural networks: algorithms and applications
inferredcites →https://doi.org/10.1109/72.286912openalex-snapshot
Inverse mapping of continuous functions using local and global information
inferredcites →https://doi.org/10.1109/72.809074openalex-snapshot
Inverting feedforward neural networks using linear and nonlinear programming
inferredcites →https://doi.org/10.1109/cvpr.2005.177openalex-snapshot
Histograms of Oriented Gradients for Human Detection
inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/cvpr.2011.5995616openalex-snapshot
Reconstructing an image from its local descriptors
inferredcites →https://doi.org/10.1109/cvpr.2014.127openalex-snapshot
Image Reconstruction from Bag-of-Visual-Words
inferredcites →https://doi.org/10.1109/cvpr.2015.7298761openalex-snapshot
Learning to generate chairs with convolutional neural networks
inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot
Fully convolutional networks for semantic segmentation
inferredcites →https://doi.org/10.1109/cvpr.2015.7299155openalex-snapshot
Understanding deep image representations by inverting them
inferredcites →https://doi.org/10.1109/iccv.2013.8openalex-snapshot
HOGgles: Visualizing Object Detection Features
inferredcites →https://doi.org/10.1109/ijcnn.1989.118277openalex-snapshot
Inversion of multilayer nets
inferredcites →https://doi.org/10.1109/tpami.2002.1017623openalex-snapshot
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
inferredcites →https://doi.org/10.1109/tpami.2009.167openalex-snapshot
Object Detection with Discriminatively Trained Part-Based Models
inferredcites →https://doi.org/10.1109/tpami.2013.228openalex-snapshot
From Bits to Images: Inversion of Local Binary Descriptors
inferredcites →https://doi.org/10.1162/neco.1989.1.4.541openalex-snapshot
Backpropagation Applied to Handwritten Zip Code Recognition
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GAN（Generative Adversarial Nets）
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Caffe: Convolutional Architecture for Fast Feature Embedding
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Distilling the Knowledge in a Neural Network
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Neural networks for pattern recognition
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Artificial intelligence in radiology – beyond the black box
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The uncertainty and explainability in object recognition
inferred← citeshttps://doi.org/10.1080/23808993.2019.1585805openalex-snapshot
Deep learning and radiomics in precision medicine
inferred← citeshttps://doi.org/10.1088/1361-6560/aaef0aopenalex-snapshot
Visually interpretable deep network for diagnosis of breast masses on mammograms
inferred← citeshttps://doi.org/10.1088/1361-6560/ab6f51openalex-snapshot
An introduction to deep learning in medical physics: advantages, potential, and challenges
inferred← citeshttps://doi.org/10.1088/1741-2552/ac4ed0openalex-snapshot
Decoding motor imagery tasks using ESI and hybrid feature CNN
inferred← citeshttps://doi.org/10.1093/bioinformatics/btac369openalex-snapshot
transferGWAS: GWAS of images using deep transfer learning
inferred← citeshttps://doi.org/10.1101/2020.05.26.117473openalex-snapshot
A Deep Feature Learning Approach for Mapping the Brain’s Microarchitecture and Organization
inferred← citeshttps://doi.org/10.1101/2024.02.28.582631openalex-snapshot
Auditory Feature-based Perceptual Distance
inferred← citeshttps://doi.org/10.1109/3dv66043.2025.00023openalex-snapshot
Obfuscation Based Privacy Preserving Representations Are Recoverable Using Neighborhood Information
inferred← citeshttps://doi.org/10.1109/access.2018.2846546openalex-snapshot
Depth Estimation of Video Sequences With Perceptual Losses
inferred← citeshttps://doi.org/10.1109/access.2019.2899901openalex-snapshot
Interpretation of Deep CNN Based on Learning Feature Reconstruction With Feedback Weights
inferred← citeshttps://doi.org/10.1109/access.2019.2958551openalex-snapshot
Clustering and Classification for Time Series Data in Visual Analytics: A Survey
inferred← citeshttps://doi.org/10.1109/access.2020.3031908openalex-snapshot
Improving the Performance of VGG Through Different Granularity Feature Combinations
inferred← citeshttps://doi.org/10.1109/access.2021.3065661openalex-snapshot
Contextual Feature Constrained Semantic Face Completion With Paired Discriminator
inferred← citeshttps://doi.org/10.1109/aiahpc66801.2025.11290501openalex-snapshot
Pixel-level crack segmentation from high-noise images using a modified fully convolutional network
inferred← citeshttps://doi.org/10.1109/apsipaasc47483.2019.9023323openalex-snapshot
Image Reconstruction from Local Descriptors Using Conditional Adversarial Networks
inferred← citeshttps://doi.org/10.1109/bigdata50022.2020.9378377openalex-snapshot
Privacy Preserving Proxy for Machine Learning as a Service
inferred← citeshttps://doi.org/10.1109/bigdata55660.2022.10020501openalex-snapshot
Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks
inferred← citeshttps://doi.org/10.1109/bigdia53151.2021.9619695openalex-snapshot
Self-interpretable Bayesian Rule Lists Model with Deep Prototype Layer
inferred← citeshttps://doi.org/10.1109/cai64502.2025.00073openalex-snapshot
Privacy-Aware Logic Free Building Automation Using Split Learning
inferred← citeshttps://doi.org/10.1109/cbmi66578.2025.11339321openalex-snapshot
Does CLIP Perceive Art the Same Way We Do?
inferred← citeshttps://doi.org/10.1109/cvpr.2016.244openalex-snapshot
iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning
inferred← citeshttps://doi.org/10.1109/cvpr.2016.254openalex-snapshot
Synthetic Data for Text Localisation in Natural Images
inferred← citeshttps://doi.org/10.1109/cvpr.2016.305openalex-snapshot
Visualizing and Understanding Deep Texture Representations
inferred← citeshttps://doi.org/10.1109/cvpr.2016.648openalex-snapshot
Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views
inferred← citeshttps://doi.org/10.1109/cvpr.2017.202openalex-snapshot
Stacked Generative Adversarial Networks
inferred← citeshttps://doi.org/10.1109/cvpr.2017.36openalex-snapshot
Diversified Texture Synthesis with Feed-Forward Networks
inferred← citeshttps://doi.org/10.1109/cvpr.2017.414openalex-snapshot
Mining Object Parts from CNNs via Active Question-Answering
inferred← citeshttps://doi.org/10.1109/cvpr.2017.434openalex-snapshot
High-Resolution Image Inpainting Using Multi-scale Neural Patch Synthesis
inferred← citeshttps://doi.org/10.1109/cvpr.2017.728openalex-snapshot
Semantic Image Inpainting with Deep Generative Models
inferred← citeshttps://doi.org/10.1109/cvpr.2017.81openalex-snapshot
The Incremental Multiresolution Matrix Factorization Algorithm
inferred← citeshttps://doi.org/10.1109/cvpr.2018.00328openalex-snapshot
What do Deep Networks Like to See?
inferred← citeshttps://doi.org/10.1109/cvpr.2018.00589openalex-snapshot
Analyzing Filters Toward Efficient ConvNet
inferred← citeshttps://doi.org/10.1109/cvpr.2018.00920openalex-snapshot
Interpretable Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1109/cvpr.2018.00948openalex-snapshot
Analytic Expressions for Probabilistic Moments of PL-DNN with Gaussian Input
inferred← citeshttps://doi.org/10.1109/cvpr.2019.00023openalex-snapshot
Revealing Scenes by Inverting Structure From Motion Reconstructions
inferred← citeshttps://doi.org/10.1109/cvpr.2019.00414openalex-snapshot
Classification-Reconstruction Learning for Open-Set Recognition
inferred← citeshttps://doi.org/10.1109/cvpr.2019.00482openalex-snapshot
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
inferred← citeshttps://doi.org/10.1109/cvpr.2019.00564openalex-snapshot
Privacy Preserving Image-Based Localization
inferred← citeshttps://doi.org/10.1109/cvpr.2019.00642openalex-snapshot
Interpreting CNNs via Decision Trees
inferred← citeshttps://doi.org/10.1109/cvpr.2019.01052openalex-snapshot
F-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning
inferred← citeshttps://doi.org/10.1109/cvpr42600.2020.00617openalex-snapshot
Vec2Face: Unveil Human Faces From Their Blackbox Features in Face Recognition
inferred← citeshttps://doi.org/10.1109/cvpr42600.2020.00869openalex-snapshot
Interpretable and Accurate Fine-grained Recognition via Region Grouping
inferred← citeshttps://doi.org/10.1109/cvpr42600.2020.00874openalex-snapshot
Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion
inferred← citeshttps://doi.org/10.1109/cvpr42600.2020.01294openalex-snapshot
Explaining Knowledge Distillation by Quantifying the Knowledge
inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.01056openalex-snapshot
Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing
inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.01060openalex-snapshot
Few-shot Image Generation via Cross-domain Correspondence
inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.01195openalex-snapshot
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks
inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.01266openalex-snapshot
Understanding Failures of Deep Networks via Robust Feature Extraction
inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.01404openalex-snapshot
Privacy-Preserving Image Features via Adversarial Affine Subspace Embeddings
inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.01469openalex-snapshot
Neural Prototype Trees for Interpretable Fine-grained Image Recognition
inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.01541openalex-snapshot
How Privacy-Preserving are Line Clouds? Recovering Scene Details from 3D Lines
inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.00723openalex-snapshot
Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detection
inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.00813openalex-snapshot
It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher
inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.00995openalex-snapshot
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning
inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.01246openalex-snapshot
NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning
inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.01682openalex-snapshot
Privacy Preserving Partial Localization
inferred← citeshttps://doi.org/10.1109/cvpr52729.2023.00794openalex-snapshot
Privacy-preserving Adversarial Facial Features
inferred← citeshttps://doi.org/10.1109/cvpr52729.2023.01262openalex-snapshot
Privacy-Preserving Representations are not Enough: Recovering Scene Content from Camera Poses
inferred← citeshttps://doi.org/10.1109/cvpr52729.2023.01476openalex-snapshot
SegLoc: Learning Segmentation-Based Representations for Privacy-Preserving Visual Localization
inferred← citeshttps://doi.org/10.1109/cvpr52729.2023.01656openalex-snapshot
Paired-Point Lifting for Enhanced Privacy-Preserving Visual Localization
inferred← citeshttps://doi.org/10.1109/cvpr52733.2024.00036openalex-snapshot
Privacy-Preserving Face Recognition Using Trainable Feature Subtraction
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