{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:Y54LRP5LRHAXOM5JMOPT64DSR5","short_pith_number":"pith:Y54LRP5L","canonical_record":{"source":{"id":"1811.09192","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-22T14:29:45Z","cross_cats_sorted":["cs.LG","cs.MM"],"title_canon_sha256":"5a5be8dea602c8d36737681a4ed7bdba821687caa064a6a570ce8ae5d5a38f86","abstract_canon_sha256":"fcde16c02b6707d5e28e45841b02fbbfc9ed76dbf92f2699b0e73a1acf8f66ee"},"schema_version":"1.0"},"canonical_sha256":"c778b8bfab89c17733a9639f3f70728f4caeba9ca68751b6e9df7015db68c309","source":{"kind":"arxiv","id":"1811.09192","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.09192","created_at":"2026-05-18T00:00:03Z"},{"alias_kind":"arxiv_version","alias_value":"1811.09192v1","created_at":"2026-05-18T00:00:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.09192","created_at":"2026-05-18T00:00:03Z"},{"alias_kind":"pith_short_12","alias_value":"Y54LRP5LRHAX","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"Y54LRP5LRHAXOM5J","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"Y54LRP5L","created_at":"2026-05-18T12:33:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:Y54LRP5LRHAXOM5JMOPT64DSR5","target":"record","payload":{"canonical_record":{"source":{"id":"1811.09192","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-22T14:29:45Z","cross_cats_sorted":["cs.LG","cs.MM"],"title_canon_sha256":"5a5be8dea602c8d36737681a4ed7bdba821687caa064a6a570ce8ae5d5a38f86","abstract_canon_sha256":"fcde16c02b6707d5e28e45841b02fbbfc9ed76dbf92f2699b0e73a1acf8f66ee"},"schema_version":"1.0"},"canonical_sha256":"c778b8bfab89c17733a9639f3f70728f4caeba9ca68751b6e9df7015db68c309","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:03.967601Z","signature_b64":"NmzuiLMHYJCEmAy4Ezpsv7GPMsQYO15gjygJQizHTECHqzcsG6DDTns9ghT0bo3DbQj9LVaMps4xrHlWt0BVBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c778b8bfab89c17733a9639f3f70728f4caeba9ca68751b6e9df7015db68c309","last_reissued_at":"2026-05-18T00:00:03.967181Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:03.967181Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.09192","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":"2026-05-18T00:00:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pvUgkGlI0HJiyIQwe/yvre6PG+GlPA1/CJEzkC2vc+3cR3RiTnxylrQLWXm8z2GESYpKIfhZ0k8IW+mvnOX/CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T22:18:35.343297Z"},"content_sha256":"d46bc7b84355aea413e2c7a5175f60414701f53a39018d10ab4120c18d789391","schema_version":"1.0","event_id":"sha256:d46bc7b84355aea413e2c7a5175f60414701f53a39018d10ab4120c18d789391"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:Y54LRP5LRHAXOM5JMOPT64DSR5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Self Paced Adversarial Training for Multimodal Few-shot Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.MM"],"primary_cat":"cs.CV","authors_text":"Frederik Pahde, Moin Nabi, Oleksiy Ostapenko, Patrick J\\\"ahnichen, Tassilo Klein","submitted_at":"2018-11-22T14:29:45Z","abstract_excerpt":"State-of-the-art deep learning algorithms yield remarkable results in many visual recognition tasks. However, they still fail to provide satisfactory results in scarce data regimes. To a certain extent this lack of data can be compensated by multimodal information. Missing information in one modality of a single data point (e.g. an image) can be made up for in another modality (e.g. a textual description). Therefore, we design a few-shot learning task that is multimodal during training (i.e. image and text) and single-modal during test time (i.e. image). In this regard, we propose a self-paced"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.09192","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":"2026-05-18T00:00:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VLTIJCQzZ5UXvBrIWLI+/SGYmCgxfSmGhSlnMeOyGUxyU72Dq4qnnxEYNcki0EgdxaJUAz0b/AP+u3DpiMkuBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T22:18:35.343964Z"},"content_sha256":"8ea0ae2c637705f3b99d939291268726f8ec5d06ff4c6f21fa6acecaef89fdae","schema_version":"1.0","event_id":"sha256:8ea0ae2c637705f3b99d939291268726f8ec5d06ff4c6f21fa6acecaef89fdae"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Y54LRP5LRHAXOM5JMOPT64DSR5/bundle.json","state_url":"https://pith.science/pith/Y54LRP5LRHAXOM5JMOPT64DSR5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Y54LRP5LRHAXOM5JMOPT64DSR5/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":"2026-05-25T22:18:35Z","links":{"resolver":"https://pith.science/pith/Y54LRP5LRHAXOM5JMOPT64DSR5","bundle":"https://pith.science/pith/Y54LRP5LRHAXOM5JMOPT64DSR5/bundle.json","state":"https://pith.science/pith/Y54LRP5LRHAXOM5JMOPT64DSR5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Y54LRP5LRHAXOM5JMOPT64DSR5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:Y54LRP5LRHAXOM5JMOPT64DSR5","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":"fcde16c02b6707d5e28e45841b02fbbfc9ed76dbf92f2699b0e73a1acf8f66ee","cross_cats_sorted":["cs.LG","cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-22T14:29:45Z","title_canon_sha256":"5a5be8dea602c8d36737681a4ed7bdba821687caa064a6a570ce8ae5d5a38f86"},"schema_version":"1.0","source":{"id":"1811.09192","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.09192","created_at":"2026-05-18T00:00:03Z"},{"alias_kind":"arxiv_version","alias_value":"1811.09192v1","created_at":"2026-05-18T00:00:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.09192","created_at":"2026-05-18T00:00:03Z"},{"alias_kind":"pith_short_12","alias_value":"Y54LRP5LRHAX","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"Y54LRP5LRHAXOM5J","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"Y54LRP5L","created_at":"2026-05-18T12:33:04Z"}],"graph_snapshots":[{"event_id":"sha256:8ea0ae2c637705f3b99d939291268726f8ec5d06ff4c6f21fa6acecaef89fdae","target":"graph","created_at":"2026-05-18T00:00:03Z","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":"State-of-the-art deep learning algorithms yield remarkable results in many visual recognition tasks. However, they still fail to provide satisfactory results in scarce data regimes. To a certain extent this lack of data can be compensated by multimodal information. Missing information in one modality of a single data point (e.g. an image) can be made up for in another modality (e.g. a textual description). Therefore, we design a few-shot learning task that is multimodal during training (i.e. image and text) and single-modal during test time (i.e. image). In this regard, we propose a self-paced","authors_text":"Frederik Pahde, Moin Nabi, Oleksiy Ostapenko, Patrick J\\\"ahnichen, Tassilo Klein","cross_cats":["cs.LG","cs.MM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-22T14:29:45Z","title":"Self Paced Adversarial Training for Multimodal Few-shot Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.09192","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:d46bc7b84355aea413e2c7a5175f60414701f53a39018d10ab4120c18d789391","target":"record","created_at":"2026-05-18T00:00:03Z","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":"fcde16c02b6707d5e28e45841b02fbbfc9ed76dbf92f2699b0e73a1acf8f66ee","cross_cats_sorted":["cs.LG","cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-22T14:29:45Z","title_canon_sha256":"5a5be8dea602c8d36737681a4ed7bdba821687caa064a6a570ce8ae5d5a38f86"},"schema_version":"1.0","source":{"id":"1811.09192","kind":"arxiv","version":1}},"canonical_sha256":"c778b8bfab89c17733a9639f3f70728f4caeba9ca68751b6e9df7015db68c309","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c778b8bfab89c17733a9639f3f70728f4caeba9ca68751b6e9df7015db68c309","first_computed_at":"2026-05-18T00:00:03.967181Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:03.967181Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NmzuiLMHYJCEmAy4Ezpsv7GPMsQYO15gjygJQizHTECHqzcsG6DDTns9ghT0bo3DbQj9LVaMps4xrHlWt0BVBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:03.967601Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.09192","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d46bc7b84355aea413e2c7a5175f60414701f53a39018d10ab4120c18d789391","sha256:8ea0ae2c637705f3b99d939291268726f8ec5d06ff4c6f21fa6acecaef89fdae"],"state_sha256":"8e89680b240c8687d9a043ade9ba47dea5d5093355bdf820298103335a1eee0d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OQ0a3BXNJl4ivjbP4rRpSNn4A2A3hRKrYgWvKXWv6pMLYdpJ+nnOhALmSphgeginN6/4inJCOo967oLMPgotAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T22:18:35.346757Z","bundle_sha256":"6f0db06e63cd0eb4491c66f2b03bdd8251cc2ca3404676f3caebbeef48804fdd"}}