PN 6-4

SimTech Project PN 6-4 "Visual Analytics for Deep Learning"

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11 to 20 of 40 Results

evaluation_data.7z Jul 25, 2023 - Model Parameters and Evaluation Data for our Visual Analysis System for Scene-Graph-Based Visual Question Answering 7Z Archive - 156.9 MB - MD5: 1900cd3a2a3a6836888073127a4978f5 Pregenerated evaluation data
graphvqa_parameters.7z Jul 25, 2023 - Model Parameters and Evaluation Data for our Visual Analysis System for Scene-Graph-Based Visual Question Answering 7Z Archive - 2.5 GB - MD5: 6521038f236d50fd2d24aad27ca30bb4 Pretrained model parameters
Supplemental Material for "Visual-Explainable AI: The Use Case of Language Models" Jul 25, 2023 Munz-Körner, Tanja; Künzel, Sebastian; Weiskopf, Daniel, 2023, "Supplemental Material for "Visual-Explainable AI: The Use Case of Language Models"", https://doi.org/10.18419/DARUS-3456, DaRUS, V1 Supplemental material for the poster "Visual-Explainable AI: The Use Case of Language Models" published at the International Conference on Data-Integrated Simulation Science 2023. Collection of videos and images showing our interactive visualization systems for exploring language models: - Text classification (https://github.com/MunzT/hiddenStatesV...
classification.mp4 Jul 25, 2023 - Supplemental Material for "Visual-Explainable AI: The Use Case of Language Models" MPEG-4 Video - 103.2 MB - MD5: 528bdd106b37c293308d8fc175ae0dd7 Video demonstrating our classification system.
Classification.zip Jul 25, 2023 - Supplemental Material for "Visual-Explainable AI: The Use Case of Language Models" ZIP Archive - 16.8 MB - MD5: 01987c2f7fe58478c03b52bdc3527c66 Screenshots of our classification system.
nmt.mp4 Jul 25, 2023 - Supplemental Material for "Visual-Explainable AI: The Use Case of Language Models" MPEG-4 Video - 94.4 MB - MD5: 870a6bea2d5039fcdf5f88694e277244 Video demonstrating our neural machine translation system.
NMT.zip Jul 25, 2023 - Supplemental Material for "Visual-Explainable AI: The Use Case of Language Models" ZIP Archive - 2.1 MB - MD5: 45e317e218545d8d33aaa99f0bb3ffef Screenshots of our neural machine translation system.
Visual Analysis System for Scene-Graph-Based Visual Question Answering Jul 25, 2023 Schäfer, Noel; Tilli, Pascal; Munz-Körner, Tanja; Künzel, Sebastian; Vidyapu, Sandeep; Vu, Ngoc Thang; Weiskopf, Daniel, 2023, "Visual Analysis System for Scene-Graph-Based Visual Question Answering", https://doi.org/10.18419/DARUS-3589, DaRUS, V1 Source code of our visual analysis system to explore scene-graph-based visual question answering. This approach is built on top of the state-of-the-art GraphVQA framework which was trained on the GQA dataset. Instructions on how to use our system can be found in the README.
GraphVQA-Explorer_v1.0.zip Jul 25, 2023 - Visual Analysis System for Scene-Graph-Based Visual Question Answering ZIP Archive - 2.7 MB - MD5: b8a072d4e77d1306ec3e027a5537c2cc Source code of our visual analytics system
NMTVis - Extended Neural Machine Translation Visualization System Jan 26, 2022 Munz, Tanja; Väth, Dirk; Kuznecov, Paul; Vu, Ngoc Thang; Weiskopf, Daniel, 2022, "NMTVis - Extended Neural Machine Translation Visualization System", https://doi.org/10.18419/DARUS-2124, DaRUS, V1 NMTVis is a web-based visual analytics system to analyze, understand, and correct translations generated with neural machine translation. First, a document can be translated using a neural machine translation model (we support an LSTM-based and the Transformer architecture). Afterward, users can find mistranslated sentences, explore and correct the...

evaluation_data.7z

Jul 25, 2023 - Model Parameters and Evaluation Data for our Visual Analysis System for Scene-Graph-Based Visual Question Answering

7Z Archive - 156.9 MB -

Pregenerated evaluation data

graphvqa_parameters.7z

Jul 25, 2023 - Model Parameters and Evaluation Data for our Visual Analysis System for Scene-Graph-Based Visual Question Answering

7Z Archive - 2.5 GB -

Pretrained model parameters

Supplemental Material for "Visual-Explainable AI: The Use Case of Language Models"

Jul 25, 2023

Munz-Körner, Tanja; Künzel, Sebastian; Weiskopf, Daniel, 2023, "Supplemental Material for "Visual-Explainable AI: The Use Case of Language Models"", https://doi.org/10.18419/DARUS-3456, DaRUS, V1

Supplemental material for the poster "Visual-Explainable AI: The Use Case of Language Models" published at the International Conference on Data-Integrated Simulation Science 2023. Collection of videos and images showing our interactive visualization systems for exploring language models: - Text classification (https://github.com/MunzT/hiddenStatesV...

classification.mp4

Jul 25, 2023 - Supplemental Material for "Visual-Explainable AI: The Use Case of Language Models"

MPEG-4 Video - 103.2 MB -

Video demonstrating our classification system.

Classification.zip

Jul 25, 2023 - Supplemental Material for "Visual-Explainable AI: The Use Case of Language Models"

ZIP Archive - 16.8 MB -

Screenshots of our classification system.

nmt.mp4

Jul 25, 2023 - Supplemental Material for "Visual-Explainable AI: The Use Case of Language Models"

MPEG-4 Video - 94.4 MB -

Video demonstrating our neural machine translation system.

NMT.zip

Jul 25, 2023 - Supplemental Material for "Visual-Explainable AI: The Use Case of Language Models"

ZIP Archive - 2.1 MB -

Screenshots of our neural machine translation system.

Visual Analysis System for Scene-Graph-Based Visual Question Answering

Jul 25, 2023

Schäfer, Noel; Tilli, Pascal; Munz-Körner, Tanja; Künzel, Sebastian; Vidyapu, Sandeep; Vu, Ngoc Thang; Weiskopf, Daniel, 2023, "Visual Analysis System for Scene-Graph-Based Visual Question Answering", https://doi.org/10.18419/DARUS-3589, DaRUS, V1

Source code of our visual analysis system to explore scene-graph-based visual question answering. This approach is built on top of the state-of-the-art GraphVQA framework which was trained on the GQA dataset. Instructions on how to use our system can be found in the README.

GraphVQA-Explorer_v1.0.zip

Jul 25, 2023 - Visual Analysis System for Scene-Graph-Based Visual Question Answering

ZIP Archive - 2.7 MB -

Source code of our visual analytics system

NMTVis - Extended Neural Machine Translation Visualization System

Jan 26, 2022

Munz, Tanja; Väth, Dirk; Kuznecov, Paul; Vu, Ngoc Thang; Weiskopf, Daniel, 2022, "NMTVis - Extended Neural Machine Translation Visualization System", https://doi.org/10.18419/DARUS-2124, DaRUS, V1

NMTVis is a web-based visual analytics system to analyze, understand, and correct translations generated with neural machine translation. First, a document can be translated using a neural machine translation model (we support an LSTM-based and the Transformer architecture). Afterward, users can find mistranslated sentences, explore and correct the...

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