{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:DAHXNLPY4FHH7U3SOMZQHQNJEI","short_pith_number":"pith:DAHXNLPY","canonical_record":{"source":{"id":"1903.11250","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-27T05:13:29Z","cross_cats_sorted":[],"title_canon_sha256":"f644d41cc844034f90d8e07668cec878d40fcd2eaec6fe24b884965037390225","abstract_canon_sha256":"f540c8e17d3ff20a8378daf86d2aeec569d2f2c2b998f5750c8d3c20d590bd2b"},"schema_version":"1.0"},"canonical_sha256":"180f76adf8e14e7fd372733303c1a9223d60bc6a0efc9424843abc0222262501","source":{"kind":"arxiv","id":"1903.11250","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.11250","created_at":"2026-05-17T23:49:54Z"},{"alias_kind":"arxiv_version","alias_value":"1903.11250v2","created_at":"2026-05-17T23:49:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.11250","created_at":"2026-05-17T23:49:54Z"},{"alias_kind":"pith_short_12","alias_value":"DAHXNLPY4FHH","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"DAHXNLPY4FHH7U3S","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"DAHXNLPY","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:DAHXNLPY4FHH7U3SOMZQHQNJEI","target":"record","payload":{"canonical_record":{"source":{"id":"1903.11250","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-27T05:13:29Z","cross_cats_sorted":[],"title_canon_sha256":"f644d41cc844034f90d8e07668cec878d40fcd2eaec6fe24b884965037390225","abstract_canon_sha256":"f540c8e17d3ff20a8378daf86d2aeec569d2f2c2b998f5750c8d3c20d590bd2b"},"schema_version":"1.0"},"canonical_sha256":"180f76adf8e14e7fd372733303c1a9223d60bc6a0efc9424843abc0222262501","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:54.746613Z","signature_b64":"YE46DHcV7Qwovt5xB6LZ8Lo2w0TU3t3nYO1+lbukUlkYTGn+xcNzOVzgukGJN3DgV9R9ovkMIfXfp/4ubjW/BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"180f76adf8e14e7fd372733303c1a9223d60bc6a0efc9424843abc0222262501","last_reissued_at":"2026-05-17T23:49:54.746215Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:54.746215Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.11250","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:49:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s75DcjFAN29RVsE3XbnmUTFgy2oreMP6ujRwtoFINFGenJpbpx9xnFpdsAmSapm9pNPyhEbpgGsT/RhZaI86Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T19:41:44.982297Z"},"content_sha256":"14b9ae6db9cabb40636f88230f76fb27c4e67b626d357b505fe097f5aae33f14","schema_version":"1.0","event_id":"sha256:14b9ae6db9cabb40636f88230f76fb27c4e67b626d357b505fe097f5aae33f14"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:DAHXNLPY4FHH7U3SOMZQHQNJEI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jian Chen, Mingkui Tan, Qi Chen, Qinfeng Shi, Qingyao Wu, Yong Guo","submitted_at":"2019-03-27T05:13:29Z","abstract_excerpt":"Generating images via the generative adversarial network (GAN) has attracted much attention recently. However, most of the existing GAN-based methods can only produce low-resolution images of limited quality. Directly generating high-resolution images using GANs is nontrivial, and often produces problematic images with incomplete objects. To address this issue, we develop a novel GAN called Auto-Embedding Generative Adversarial Network (AEGAN), which simultaneously encodes the global structure features and captures the fine-grained details. In our network, we use an autoencoder to learn the in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.11250","kind":"arxiv","version":2},"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":"2026-05-17T23:49:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qtsa0fMHKs/brOSdExY01ncsFgkhaMIzOZjC+EOV4SOTAlPqYsXyYIrMXeQxe6ce/V0ftQnH8Ap54arxQmKyBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T19:41:44.982645Z"},"content_sha256":"8233596447598a87b51aee2208ecd2359407ec2d44fe6118d505fa78ab95a954","schema_version":"1.0","event_id":"sha256:8233596447598a87b51aee2208ecd2359407ec2d44fe6118d505fa78ab95a954"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DAHXNLPY4FHH7U3SOMZQHQNJEI/bundle.json","state_url":"https://pith.science/pith/DAHXNLPY4FHH7U3SOMZQHQNJEI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DAHXNLPY4FHH7U3SOMZQHQNJEI/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":"2026-06-01T19:41:44Z","links":{"resolver":"https://pith.science/pith/DAHXNLPY4FHH7U3SOMZQHQNJEI","bundle":"https://pith.science/pith/DAHXNLPY4FHH7U3SOMZQHQNJEI/bundle.json","state":"https://pith.science/pith/DAHXNLPY4FHH7U3SOMZQHQNJEI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DAHXNLPY4FHH7U3SOMZQHQNJEI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:DAHXNLPY4FHH7U3SOMZQHQNJEI","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":"f540c8e17d3ff20a8378daf86d2aeec569d2f2c2b998f5750c8d3c20d590bd2b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-27T05:13:29Z","title_canon_sha256":"f644d41cc844034f90d8e07668cec878d40fcd2eaec6fe24b884965037390225"},"schema_version":"1.0","source":{"id":"1903.11250","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.11250","created_at":"2026-05-17T23:49:54Z"},{"alias_kind":"arxiv_version","alias_value":"1903.11250v2","created_at":"2026-05-17T23:49:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.11250","created_at":"2026-05-17T23:49:54Z"},{"alias_kind":"pith_short_12","alias_value":"DAHXNLPY4FHH","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"DAHXNLPY4FHH7U3S","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"DAHXNLPY","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:8233596447598a87b51aee2208ecd2359407ec2d44fe6118d505fa78ab95a954","target":"graph","created_at":"2026-05-17T23:49:54Z","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":"Generating images via the generative adversarial network (GAN) has attracted much attention recently. However, most of the existing GAN-based methods can only produce low-resolution images of limited quality. Directly generating high-resolution images using GANs is nontrivial, and often produces problematic images with incomplete objects. To address this issue, we develop a novel GAN called Auto-Embedding Generative Adversarial Network (AEGAN), which simultaneously encodes the global structure features and captures the fine-grained details. In our network, we use an autoencoder to learn the in","authors_text":"Jian Chen, Mingkui Tan, Qi Chen, Qinfeng Shi, Qingyao Wu, Yong Guo","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-27T05:13:29Z","title":"Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.11250","kind":"arxiv","version":2},"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":"sha256:14b9ae6db9cabb40636f88230f76fb27c4e67b626d357b505fe097f5aae33f14","target":"record","created_at":"2026-05-17T23:49:54Z","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":"f540c8e17d3ff20a8378daf86d2aeec569d2f2c2b998f5750c8d3c20d590bd2b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-27T05:13:29Z","title_canon_sha256":"f644d41cc844034f90d8e07668cec878d40fcd2eaec6fe24b884965037390225"},"schema_version":"1.0","source":{"id":"1903.11250","kind":"arxiv","version":2}},"canonical_sha256":"180f76adf8e14e7fd372733303c1a9223d60bc6a0efc9424843abc0222262501","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"180f76adf8e14e7fd372733303c1a9223d60bc6a0efc9424843abc0222262501","first_computed_at":"2026-05-17T23:49:54.746215Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:49:54.746215Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YE46DHcV7Qwovt5xB6LZ8Lo2w0TU3t3nYO1+lbukUlkYTGn+xcNzOVzgukGJN3DgV9R9ovkMIfXfp/4ubjW/BA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:49:54.746613Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.11250","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:14b9ae6db9cabb40636f88230f76fb27c4e67b626d357b505fe097f5aae33f14","sha256:8233596447598a87b51aee2208ecd2359407ec2d44fe6118d505fa78ab95a954"],"state_sha256":"fa88374ecd0bfc280c24b91f481f30642f873d8efd5681c0c21e8aa3953e9dfc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7DpNyhYtbG9z5YxiYXwu3bPufDpa8wmwwdaQPUphe9wMFmrJNza+KHCaGAsCj93WYKIwN8KGhf7xom7pr4qGBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T19:41:44.984499Z","bundle_sha256":"8168ccde959d759ed2432d43521f988c4f9f7be2ad8a8dfb7c70de912ecc5f28"}}