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2004.13166
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A Disentangling Invertible Interpretation Network for Explaining Latent Representations
Computer Vision and Pattern Recognition (CVPR), 2020
27 April 2020
Patrick Esser
Robin Rombach
Bjorn Ommer
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Papers citing
"A Disentangling Invertible Interpretation Network for Explaining Latent Representations"
50 / 64 papers shown
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Constructing Fair Latent Space for Intersection of Fairness and Explainability
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Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition
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Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations
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Anish Kachinthaya
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Yossi Gandelsman
VLM
457
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Formation of Representations in Neural Networks
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Liu Ziyin
Isaac Chuang
Tomer Galanti
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Concept-Based Explanations in Computer Vision: Where Are We and Where Could We Go?
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Georgii Mikriukov
Gesina Schwalbe
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385
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Enabling Regional Explainability by Automatic and Model-agnostic Rule Extraction
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Tianyu Cui
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Nan Fletcher-Loyd
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LatentExplainer: Explaining Latent Representations in Deep Generative Models with Multimodal Large Language Models
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The Anatomy of Adversarial Attacks: Concept-based XAI Dissection
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Gesina Schwalbe
Franz Motzkus
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On the Challenges and Opportunities in Generative AI
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Kushagra Pandey
Kushagra Pandey
Robert Bamler
Sina Daubener
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F. Wenzel
Frank Wood
Stephan Mandt
Vincent Fortuin
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Interpreting Differentiable Latent States for Healthcare Time-series Data
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Nivedita Bijlani
Samaneh Kouchaki
Payam Barnaghi
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TextCLIP: Text-Guided Face Image Generation And Manipulation Without Adversarial Training
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Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learning
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Stefano Teso
376
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317
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Rosetta Neurons: Mining the Common Units in a Model Zoo
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Amil Dravid
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Learning Disentangled Semantic Spaces of Explanations via Invertible Neural Networks
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305
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Texture Learning Domain Randomization for Domain Generalized Segmentation
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Dae-Hwan Kim
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439
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Generative Semantic Segmentation
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249
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Connecting metrics for shape-texture knowledge in computer vision
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Tiago Marques
Arlindo L. Oliveira
145
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Local Manifold Augmentation for Multiview Semantic Consistency
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341
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Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks
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M. Kowal
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Md. Amirul Islam
Neil D. B. Bruce
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A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information
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Md. Amirul Islam
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356
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Concept Embedding Analysis: A Review
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Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning
European Conference on Computer Vision (ECCV), 2022
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Towards Disentangling Information Paths with Coded ResNeXt
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From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
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