{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:AFYJ3MGPFSU64Y5SEAZOS3FOVQ","short_pith_number":"pith:AFYJ3MGP","canonical_record":{"source":{"id":"1809.04747","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-13T02:37:53Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"50998199acb040243330241b1a574c775073f7dce1658b23476ffd967c984d21","abstract_canon_sha256":"4bcc2310c3aa24441891b6b115dbd1098d851487870dcb7448616c19cc8cd436"},"schema_version":"1.0"},"canonical_sha256":"01709db0cf2ca9ee63b22032e96caeac25c4ed400858a267cb2c3529fd4acf59","source":{"kind":"arxiv","id":"1809.04747","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.04747","created_at":"2026-05-18T00:05:49Z"},{"alias_kind":"arxiv_version","alias_value":"1809.04747v1","created_at":"2026-05-18T00:05:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.04747","created_at":"2026-05-18T00:05:49Z"},{"alias_kind":"pith_short_12","alias_value":"AFYJ3MGPFSU6","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_16","alias_value":"AFYJ3MGPFSU64Y5S","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_8","alias_value":"AFYJ3MGP","created_at":"2026-05-18T12:32:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:AFYJ3MGPFSU64Y5SEAZOS3FOVQ","target":"record","payload":{"canonical_record":{"source":{"id":"1809.04747","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-13T02:37:53Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"50998199acb040243330241b1a574c775073f7dce1658b23476ffd967c984d21","abstract_canon_sha256":"4bcc2310c3aa24441891b6b115dbd1098d851487870dcb7448616c19cc8cd436"},"schema_version":"1.0"},"canonical_sha256":"01709db0cf2ca9ee63b22032e96caeac25c4ed400858a267cb2c3529fd4acf59","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:49.749170Z","signature_b64":"bNr0XEBD1yjFSqxv7MBtYq8GMygNJcKwFlI9/F1VKsJNZnwxkjq+fuBGnFWBCzNUltJg5aNqfwnJE9Lz+cFABQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01709db0cf2ca9ee63b22032e96caeac25c4ed400858a267cb2c3529fd4acf59","last_reissued_at":"2026-05-18T00:05:49.748642Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:49.748642Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.04747","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:05:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GELEs8GDgD8otbQbOk+J/K8MBTcC9dbm1OopYvvDJuPZUeWqrc3YxrQ2pa8HviEuh45CrVEJS3uHil89zBTgBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T06:05:24.158569Z"},"content_sha256":"53d0b5acd3c6ff99f1e64ba2b4048601782b7997ee588799eadb971dc5ec7dd3","schema_version":"1.0","event_id":"sha256:53d0b5acd3c6ff99f1e64ba2b4048601782b7997ee588799eadb971dc5ec7dd3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:AFYJ3MGPFSU64Y5SEAZOS3FOVQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Geodesic Clustering in Deep Generative Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Dongmei Fu, Georgios Arvanitidis, S{\\o}ren Hauberg, Tao Yang, Xiaogang Li","submitted_at":"2018-09-13T02:37:53Z","abstract_excerpt":"Deep generative models are tremendously successful in learning low-dimensional latent representations that well-describe the data. These representations, however, tend to much distort relationships between points, i.e. pairwise distances tend to not reflect semantic similarities well. This renders unsupervised tasks, such as clustering, difficult when working with the latent representations. We demonstrate that taking the geometry of the generative model into account is sufficient to make simple clustering algorithms work well over latent representations. Leaning on the recent finding that dee"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.04747","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:05:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/duHb4s+5AQOQMAUK+ybk6e41o1P/6sD/TqRaftl3xwv+gdxbR8cwv+mbRISuKYsoZvT896g8XNNN77ne0BLAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T06:05:24.158923Z"},"content_sha256":"eb4ff091530bb081dcbabc97bfb1b254aca0f151d049fac2c8392668c1c5e173","schema_version":"1.0","event_id":"sha256:eb4ff091530bb081dcbabc97bfb1b254aca0f151d049fac2c8392668c1c5e173"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AFYJ3MGPFSU64Y5SEAZOS3FOVQ/bundle.json","state_url":"https://pith.science/pith/AFYJ3MGPFSU64Y5SEAZOS3FOVQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AFYJ3MGPFSU64Y5SEAZOS3FOVQ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-21T06:05:24Z","links":{"resolver":"https://pith.science/pith/AFYJ3MGPFSU64Y5SEAZOS3FOVQ","bundle":"https://pith.science/pith/AFYJ3MGPFSU64Y5SEAZOS3FOVQ/bundle.json","state":"https://pith.science/pith/AFYJ3MGPFSU64Y5SEAZOS3FOVQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AFYJ3MGPFSU64Y5SEAZOS3FOVQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:AFYJ3MGPFSU64Y5SEAZOS3FOVQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"4bcc2310c3aa24441891b6b115dbd1098d851487870dcb7448616c19cc8cd436","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-13T02:37:53Z","title_canon_sha256":"50998199acb040243330241b1a574c775073f7dce1658b23476ffd967c984d21"},"schema_version":"1.0","source":{"id":"1809.04747","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.04747","created_at":"2026-05-18T00:05:49Z"},{"alias_kind":"arxiv_version","alias_value":"1809.04747v1","created_at":"2026-05-18T00:05:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.04747","created_at":"2026-05-18T00:05:49Z"},{"alias_kind":"pith_short_12","alias_value":"AFYJ3MGPFSU6","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_16","alias_value":"AFYJ3MGPFSU64Y5S","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_8","alias_value":"AFYJ3MGP","created_at":"2026-05-18T12:32:13Z"}],"graph_snapshots":[{"event_id":"sha256:eb4ff091530bb081dcbabc97bfb1b254aca0f151d049fac2c8392668c1c5e173","target":"graph","created_at":"2026-05-18T00:05:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Deep generative models are tremendously successful in learning low-dimensional latent representations that well-describe the data. These representations, however, tend to much distort relationships between points, i.e. pairwise distances tend to not reflect semantic similarities well. This renders unsupervised tasks, such as clustering, difficult when working with the latent representations. We demonstrate that taking the geometry of the generative model into account is sufficient to make simple clustering algorithms work well over latent representations. Leaning on the recent finding that dee","authors_text":"Dongmei Fu, Georgios Arvanitidis, S{\\o}ren Hauberg, Tao Yang, Xiaogang Li","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-13T02:37:53Z","title":"Geodesic Clustering in Deep Generative Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.04747","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:53d0b5acd3c6ff99f1e64ba2b4048601782b7997ee588799eadb971dc5ec7dd3","target":"record","created_at":"2026-05-18T00:05:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"4bcc2310c3aa24441891b6b115dbd1098d851487870dcb7448616c19cc8cd436","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-13T02:37:53Z","title_canon_sha256":"50998199acb040243330241b1a574c775073f7dce1658b23476ffd967c984d21"},"schema_version":"1.0","source":{"id":"1809.04747","kind":"arxiv","version":1}},"canonical_sha256":"01709db0cf2ca9ee63b22032e96caeac25c4ed400858a267cb2c3529fd4acf59","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"01709db0cf2ca9ee63b22032e96caeac25c4ed400858a267cb2c3529fd4acf59","first_computed_at":"2026-05-18T00:05:49.748642Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:05:49.748642Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bNr0XEBD1yjFSqxv7MBtYq8GMygNJcKwFlI9/F1VKsJNZnwxkjq+fuBGnFWBCzNUltJg5aNqfwnJE9Lz+cFABQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:05:49.749170Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.04747","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:53d0b5acd3c6ff99f1e64ba2b4048601782b7997ee588799eadb971dc5ec7dd3","sha256:eb4ff091530bb081dcbabc97bfb1b254aca0f151d049fac2c8392668c1c5e173"],"state_sha256":"3dffd61c4858af3a71a0c355262cc4e3525f2b4236f1d9206f1fde00f68d28d7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BzFE0+MKXIEuPUU5fMnWkVEvaMgAjQWAmhOkVZtWKkJyE+pj37DCK6/I0geb/2HEL61K1NDJTYfA1SNHvHRmBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T06:05:24.161225Z","bundle_sha256":"c772656fa5a7ecb8e3904e1f5d93e2e03f8d85d01bd0806c6a334ba9ff06ae36"}}