GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors | Paper Citation Record

Observation 09b1dbd8-dda2-4f41-877c-c77751092bdf · outbound

This paper cites Generative adversarial nets.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Generative adversarial nets

Reference 1

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Observation c5332b14-7fce-4f59-afef-0e7db65d6f4b · outbound

This paper cites A style-based generator architecture for generative adversarial networks.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors A style-based generator architecture for generative adversarial networks

Reference 2

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Observation ea395529-2360-40d9-9e07-57c229c1835b · outbound

This paper cites Analyzing and improving the image quality of StyleGAN.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Analyzing and improving the image quality of StyleGAN

Reference 3

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Observation cca521a6-b265-47c7-addd-c7387cdf0fc7 · outbound

This paper cites Denoising diffusion probabilistic models.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Denoising diffusion probabilistic models

Reference 4

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Observation bcb98d69-91c5-449e-a81c-8d6866d0a508 · outbound

This paper cites Diffusion models beat GANs on image synthesis.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Diffusion models beat GANs on image synthesis

Reference 5

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Observation 756aa1ae-8f41-4dd2-b7f7-02ddbfa9683a · outbound

This paper cites High- resolution image synthesis with latent diffusion models.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors High- resolution image synthesis with latent diffusion models

Reference 6

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Observation 2e491179-418f-4f3e-a6ed-f886aafba415 · outbound

This paper cites Learning transferable visual models from natural language supervision.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Learning transferable visual models from natural language supervision

Reference 7

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Observation 45c997a5-3242-4b0f-828a-fb0525610711 · outbound

This paper cites FaceForensics++: Learning to detect manipulated facial images.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors FaceForensics++: Learning to detect manipulated facial images

Reference 8

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Observation 94648a4e-bb09-40d9-89cd-cebdb86939e8 · outbound

This paper cites Orthogonal subspace decomposition for generalizable AI-generated image detection.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Orthogonal subspace decomposition for generalizable AI-generated image detection

Reference 9

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Observation 6031dccb-836f-417a-b9d2-645c0dc6ed14 · outbound

This paper cites Towards universal fake image detectors that generalize across generative models.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Towards universal fake image detectors that generalize across generative models

Reference 10

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Observation 16f02f86-9f40-4870-a202-81158840a27c · outbound

This paper cites an unresolved cited work.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Unresolved cited work

Reference 11

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Observation c6c65c0e-5716-46d6-8e95-d9bb14ec4a80 · outbound

This paper cites When Detectors Forget Forensics: Blocking Semantic Shortcuts for Generalizable AI-Generated Image Detection.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors When Detectors Forget Forensics: Blocking Semantic Shortcuts for Generalizable AI-Generated Image Detection

Reference 12

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Observation 15b096a1-6554-4656-89bb-454173b46de8 · outbound

This paper cites DINOv3.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors DINOv3

Reference 13

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Observation 21608b02-8758-455c-a2ee-5a82c3b35e3f · outbound

This paper cites MesoNet: A compact facial video forgery detection network.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors MesoNet: A compact facial video forgery detection network

Reference 14

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Observation a60cd5e3-1e99-4156-993d-244f040a9106 · outbound

This paper cites Thinking in frequency: Face forgery detection by mining frequency-aware clues.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Thinking in frequency: Face forgery detection by mining frequency-aware clues

Reference 15

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Observation f4a8e78a-2740-4f62-968a-3d8335cbf66e · outbound

This paper cites Spatial-phase shallow learning: Rethinking face forgery detection in fre- quency domain.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Spatial-phase shallow learning: Rethinking face forgery detection in fre- quency domain

Reference 16

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Observation aff7dc69-65c0-4e21-8a47-96b9d4b6edb8 · outbound

This paper cites Face X-ray for more general face forgery detection.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Face X-ray for more general face forgery detection

Reference 17

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Observation bcc1a5ad-d5f8-4ec0-88aa-47c209283af2 · outbound

This paper cites What makes fake images detectable? Understanding properties that generalize.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors What makes fake images detectable? Understanding properties that generalize

Reference 18

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Observation ebb9012c-c61b-4482-8b6a-7786c9894bba · outbound

This paper cites Leveraging representations from intermediate encoder-blocks for synthetic image detection.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Leveraging representations from intermediate encoder-blocks for synthetic image detection

Reference 19

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Observation 1681b635-8212-4ebb-84f1-37edec15d803 · outbound

This paper cites Detecting deepfakes with self-blended images.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Detecting deepfakes with self-blended images

Reference 20

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Observation 83b472a6-4f04-4e77-b00b-7e7d89b70dbd · outbound

This paper cites Lips don’t lie: A generalisable and robust approach to face forgery detection.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Lips don’t lie: A generalisable and robust approach to face forgery detection

Reference 21

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Observation 04465163-1140-44a3-89c6-95bb6e8fa80a · outbound

This paper cites Exploring temporal coherence for more general video face forgery detection.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Exploring temporal coherence for more general video face forgery detection

Reference 22

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Observation 9d861906-8511-412c-9337-e26ea052417a · outbound

This paper cites Leveraging real talking faces via self-supervision for robust forgery detection.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Leveraging real talking faces via self-supervision for robust forgery detection

Reference 23

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Observation 8db2d223-226e-4306-9083-fb6b3247a3e3 · outbound

This paper cites Rethinking the up-sampling operations in CNN-based generative network for generalizable deepfake detection.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Rethinking the up-sampling operations in CNN-based generative network for generalizable deepfake detection

Reference 24

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Observation 6bf810c9-15b8-489a-85fc-002bb6a9e3f0 · outbound

This paper cites DIRE for diffusion-generated image detection.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors DIRE for diffusion-generated image detection

Reference 25

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Observation ba7cab43-db21-4f35-84fc-b3b3c394d880 · outbound

This paper cites Exposing the Fake: Effective Diffusion-Generated Images Detection.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Exposing the Fake: Effective Diffusion-Generated Images Detection

Reference 26

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Observation f3ba6dc4-967c-4c17-953a-08d7255e3a3d · outbound

This paper cites LaRE 2: Latent reconstruction error based method for diffusion-generated image detection.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors LaRE 2: Latent reconstruction error based method for diffusion-generated image detection

Reference 27

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Observation 6eaff946-6949-44c7-a185-8ff9cc1a61d4 · outbound

This paper cites AEROBLADE: Training-free detection of latent diffusion images using autoencoder reconstruction error.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors AEROBLADE: Training-free detection of latent diffusion images using autoencoder reconstruction error

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Observation ae675d92-98c2-40f5-9f75-d54d2b275866 · outbound

This paper cites RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection

Reference 29

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Observation 2bf48227-4e13-4905-9765-a52dfbe035a7 · outbound

This paper cites Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models

Reference 30

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Observation bcbcb762-be98-4d0f-b08f-8f8bff99ccc2 · outbound

This paper cites Manifold induced biases for zero-shot and few-shot detection of generated images.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Manifold induced biases for zero-shot and few-shot detection of generated images

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Observation 27dbda00-b63d-47b5-bee8-0310165e2d77 · outbound

This paper cites Dimensional Coactivation for Representational Consistency in Frozen Vision Foundation Models.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Dimensional Coactivation for Representational Consistency in Frozen Vision Foundation Models

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Observation 2d0f9285-8c9f-4aaa-9e94-ec8aff29ee89 · outbound

This paper cites SPLIT: Training-Free AI-Generated and Partially Edited Video Detection via Spatial Patch-Level Incoherence and Temporal Roughness.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors SPLIT: Training-Free AI-Generated and Partially Edited Video Detection via Spatial Patch-Level Incoherence and Temporal Roughness

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Observation 98a25c3f-d58b-42b3-afb3-c4f04eb1896f · outbound

This paper cites Rethinking cross-generator image forgery detection through DINOv3.arXiv preprint arXiv:2511.22471, 2025.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Rethinking cross-generator image forgery detection through DINOv3.arXiv preprint arXiv:2511.22471, 2025

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Observation fa53dc28-ce66-49ff-b305-59d5979a9d1d · outbound

This paper cites How Fragile Are Training-Free AI-Generated Image Detectors? A Controlled Audit of Score Direction, Preprocessing, and Compression.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors How Fragile Are Training-Free AI-Generated Image Detectors? A Controlled Audit of Score Direction, Preprocessing, and Compression

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Observation d5ac794d-3c2a-4cda-a475-d78e2b6120f6 · outbound

This paper cites Estimating the intrinsic dimension of datasets by a minimal neighborhood information.Scientific Reports, 7:12140, 2017.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Estimating the intrinsic dimension of datasets by a minimal neighborhood information.Scientific Reports, 7:12140, 2017

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Observation 1f893577-f409-4004-8315-4788bba89375 · outbound

This paper cites Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental Analysis.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental Analysis

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Observation ebe2e855-3bd6-426c-b743-d14fcb6e569c · outbound

This paper cites Macke, and Davide Zoccolan.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Macke, and Davide Zoccolan

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Observation 1db31497-9e82-4a34-b051-4dcc6ddec606 · outbound

This paper cites The intrinsic dimension of images and its impact on learning.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors The intrinsic dimension of images and its impact on learning

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Observation b50b2af1-de17-4e17-ab77-22e863e4df51 · outbound

This paper cites Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E

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Observation 4fb9a104-dfa2-4a62-8b8a-2be6b60ee796 · outbound

This paper cites Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection

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Observation 3c51967b-caae-4774-9f92-ee6df22a18ab · outbound

This paper cites Intrinsic dimension estimation for robust detection of AI-generated texts.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Intrinsic dimension estimation for robust detection of AI-generated texts

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Observation bd015e5a-7d2b-49ec-b663-2910f2d61bd9 · outbound

This paper cites Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality

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Observation 7fc2376f-cfc1-4171-978b-08147f805fdd · outbound

This paper cites Generative image inpainting with submanifold alignment.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Generative image inpainting with submanifold alignment

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Observation b8c1c85d-8952-40ac-a0c9-f5fda66f21da · outbound

This paper cites Rethinking the use of vision transformers for AI-generated image detection.arXiv preprint arXiv:2512.04969, 2025.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Rethinking the use of vision transformers for AI-generated image detection.arXiv preprint arXiv:2512.04969, 2025

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Observation 4ed493aa-d67f-48a7-b933-88cfff72a7d5 · outbound

This paper cites Intermediate Representations are Strong AI-Generated Image Detectors.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Intermediate Representations are Strong AI-Generated Image Detectors

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Observation 61510ce5-14f2-4875-ad1e-cd43877f1d5d · outbound

This paper cites When semantics regulate: Rethinking patch shuffle and internal bias for generated image detection with CLIP.arXiv preprint arXiv:2511.19126, 2025.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors When semantics regulate: Rethinking patch shuffle and internal bias for generated image detection with CLIP.arXiv preprint arXiv:2511.19126, 2025

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Observation d8066b30-7737-4f7f-8387-f0e4b128efd7 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors An image is worth 16x16 words: Transformers for image recognition at scale

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Observation d8c4326c-c870-4689-a951-cbc3aa04d3dd · outbound

This paper cites YuNet: A tiny millisecond-level face detector.Machine Intelligence Research, 20(5):656–665, 2023.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors YuNet: A tiny millisecond-level face detector.Machine Intelligence Research, 20(5):656–665, 2023

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Observation 96145e14-01d7-4ac5-8dd3-3ddfad1040e1 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

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Observation 52d9d352-928a-48b6-a08c-7fa10647d41d · outbound

This paper cites Deep learning face attributes in the wild.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Deep learning face attributes in the wild

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Observation f4e1f6ef-7923-4913-8463-a11b3c2f7950 · outbound

This paper cites Celeb-DF: A large-scale challeng- ing dataset for DeepFake forensics.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Celeb-DF: A large-scale challeng- ing dataset for DeepFake forensics

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Observation 52160961-3415-44df-9519-572a7ed9cb66 · outbound

This paper cites DF40: Toward next-generation deepfake detection.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors DF40: Toward next-generation deepfake detection

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Observation 6d8f0e20-f1cb-4481-be07-7040eb691bf5 · outbound

This paper cites SimSwap: An efficient framework for high fidelity face swapping.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors SimSwap: An efficient framework for high fidelity face swapping

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Observation 05a07cfc-ca13-4fe9-a2db-91331faaf944 · outbound

This paper cites BlendFace: Re-designing identity encoders for face-swapping.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors BlendFace: Re-designing identity encoders for face-swapping

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Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:54.550136Z

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Observation 4fb5031b-29a1-4d74-839e-9921184b1a6f · outbound

This paper cites First order motion model for image animation.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors First order motion model for image animation

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:54.618897Z

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Observation 75c4b748-7dd1-4dee-a237-0bbae4b994a3 · outbound

This paper cites SadTalker: Learning realistic 3D motion coefficients for stylized audio-driven single image talking face animation.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors SadTalker: Learning realistic 3D motion coefficients for stylized audio-driven single image talking face animation

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:54.739648Z

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Observation ac046c62-f5b5-4f4c-8a18-9471306f3125 · outbound

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GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Unresolved cited work

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:54.823180Z

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Observation 89a3827c-27f4-47c8-b79a-d74adc21337f · outbound

This paper cites Thin-plate spline motion model for image animation.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Thin-plate spline motion model for image animation

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:54.894167Z

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Observation f6820c90-6346-4fb9-b0ce-8f4678fa9aca · outbound

This paper cites The DeepFake Detection Challenge (DFDC) Dataset.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors The DeepFake Detection Challenge (DFDC) Dataset

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:54.947851Z

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Observation 86f79653-b844-4e89-873c-2ddf572bb4cc · outbound

This paper cites WildDeepfake: A challenging real-world dataset for deepfake detection.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors WildDeepfake: A challenging real-world dataset for deepfake detection

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:55.038974Z

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Observation 00176763-e4c5-4a59-be15-b245b822597e · outbound

This paper cites V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, et al.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, et al

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:55.100936Z

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Observation 4b2f4329-ac50-48e5-acea-85b2c797891e · outbound

This paper cites EVA-02: A Visual Representation for Neon Genesis.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors EVA-02: A Visual Representation for Neon Genesis

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:55.183097Z

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Observation 87d558dc-e124-488e-b454-4424af3b1e69 · outbound

This paper cites Fine-tuning can distort pretrained features and underperform out-of-distribution.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Fine-tuning can distort pretrained features and underperform out-of-distribution

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:55.238334Z

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Observation c12fcca0-9786-4224-bc2b-e220d736e637 · outbound

This paper cites Gradient starvation: A learning proclivity in neural networks.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Gradient starvation: A learning proclivity in neural networks

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:55.297477Z

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Observation 6c1edd63-7c26-47f7-a22b-012ebc321f04 · outbound

This paper cites Progressive growing of GANs for improved quality, stability, and variation.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Progressive growing of GANs for improved quality, stability, and variation

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:55.350578Z

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Observation 9bd6dac9-a72c-44c3-a7e5-eae12c1a3167 · outbound

This paper cites an unresolved cited work.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Unresolved cited work

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:55.411508Z

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Observation 327cda00-6098-4d06-a2c1-bc2cf7c22b65 · outbound

This paper cites On the stability of fine- tuning BERT: Misconceptions, explanations, and strong baselines.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors On the stability of fine- tuning BERT: Misconceptions, explanations, and strong baselines

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:55.529202Z

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Observation 5751f1c2-e360-4468-8265-cd588822b662 · outbound

This paper cites Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:55.587246Z

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Observation 30e26e14-68ad-42be-beb0-fd040d90b228 · outbound

This paper cites Weinberger.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Weinberger

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:55.671091Z

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Observation b35cbfed-be5d-4610-8ee1-e01d330355a9 · outbound

This paper cites Reti- naFace: Single-stage dense face localisation in the wild.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Reti- naFace: Single-stage dense face localisation in the wild

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:55.762604Z

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Observation a1a42431-ee0b-4ccc-8ce3-99f27ed8503e · outbound

This paper cites General facial representation learning in a visual- linguistic manner.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors General facial representation learning in a visual- linguistic manner

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:55.845414Z

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