{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:I65C2IN3Q6DKH3OLTM4IXOQ2FM","short_pith_number":"pith:I65C2IN3","canonical_record":{"source":{"id":"1704.02906","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-10T15:26:23Z","cross_cats_sorted":["cs.AI","cs.GR","cs.LG","stat.ML"],"title_canon_sha256":"1828d83212f2e58fb0ed94a79b526ea0044243b740730942fd66aad63f61ff72","abstract_canon_sha256":"6714c6f1ca975462a3c168481ed55b6aa770112ccd8b1ef12d5603dc07a23556"},"schema_version":"1.0"},"canonical_sha256":"47ba2d21bb8786a3edcb9b388bba1a2b3f2d6fd98f6ff1db23ec4c528b01fac8","source":{"kind":"arxiv","id":"1704.02906","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.02906","created_at":"2026-05-18T00:10:47Z"},{"alias_kind":"arxiv_version","alias_value":"1704.02906v3","created_at":"2026-05-18T00:10:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.02906","created_at":"2026-05-18T00:10:47Z"},{"alias_kind":"pith_short_12","alias_value":"I65C2IN3Q6DK","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"I65C2IN3Q6DKH3OL","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"I65C2IN3","created_at":"2026-05-18T12:31:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:I65C2IN3Q6DKH3OLTM4IXOQ2FM","target":"record","payload":{"canonical_record":{"source":{"id":"1704.02906","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-10T15:26:23Z","cross_cats_sorted":["cs.AI","cs.GR","cs.LG","stat.ML"],"title_canon_sha256":"1828d83212f2e58fb0ed94a79b526ea0044243b740730942fd66aad63f61ff72","abstract_canon_sha256":"6714c6f1ca975462a3c168481ed55b6aa770112ccd8b1ef12d5603dc07a23556"},"schema_version":"1.0"},"canonical_sha256":"47ba2d21bb8786a3edcb9b388bba1a2b3f2d6fd98f6ff1db23ec4c528b01fac8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:47.812766Z","signature_b64":"1BTcxzngjd/5U+Z5McD2QsSncNN+15PmpBRYn0r4gTbCiLigVwFJ56QgnUXBHPxRoShOn/coG+bW3WcU16rOCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47ba2d21bb8786a3edcb9b388bba1a2b3f2d6fd98f6ff1db23ec4c528b01fac8","last_reissued_at":"2026-05-18T00:10:47.812116Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:47.812116Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1704.02906","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:10:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8cmzD8SnkjgBZZiW5aB6Y7XUQpDZ0O8eS7SOi7yX/vHYdBOq3DmFPU+wxDYCHFfs+RJMeQjfsM2WcGRCDaRbBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T22:33:42.111008Z"},"content_sha256":"d18d31139080058df6570a87c7dbee5da1b563cd281bc443b956b4a4297effa7","schema_version":"1.0","event_id":"sha256:d18d31139080058df6570a87c7dbee5da1b563cd281bc443b956b4a4297effa7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:I65C2IN3Q6DKH3OLTM4IXOQ2FM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-Agent Diverse Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.GR","cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Arnab Ghosh, Philip H. S. Torr, Puneet K. Dokania, Vinay Namboodiri, Viveka Kulharia","submitted_at":"2017-04-10T15:26:23Z","abstract_excerpt":"We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks (GANs) and its conditional variants to address the well known problem of mode collapse. First, MAD-GAN is a multi-agent GAN architecture incorporating multiple generators and one discriminator. Second, to enforce that different generators capture diverse high probability modes, the discriminator of MAD-GAN is designed such that along with finding the real and fake samples, it is also required to identify the generator that generated the given fake sample. Intuitively, to succeed in this task, the discriminat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.02906","kind":"arxiv","version":3},"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":"2026-05-18T00:10:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EVxoQ4hgEIkJ2Vt58dv7rCjCkN2/kFDtdDr5c0D4gfBbHpc8+iEj13uHfYpzxzo/gWvLGF+j7jNODWGJk9dKCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T22:33:42.111368Z"},"content_sha256":"6714e7d9ac9aa5d7773b3b62a0b134a3f2b15a6f169f882ab2d6695b98a0c41c","schema_version":"1.0","event_id":"sha256:6714e7d9ac9aa5d7773b3b62a0b134a3f2b15a6f169f882ab2d6695b98a0c41c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I65C2IN3Q6DKH3OLTM4IXOQ2FM/bundle.json","state_url":"https://pith.science/pith/I65C2IN3Q6DKH3OLTM4IXOQ2FM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I65C2IN3Q6DKH3OLTM4IXOQ2FM/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":"2026-06-08T22:33:42Z","links":{"resolver":"https://pith.science/pith/I65C2IN3Q6DKH3OLTM4IXOQ2FM","bundle":"https://pith.science/pith/I65C2IN3Q6DKH3OLTM4IXOQ2FM/bundle.json","state":"https://pith.science/pith/I65C2IN3Q6DKH3OLTM4IXOQ2FM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I65C2IN3Q6DKH3OLTM4IXOQ2FM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:I65C2IN3Q6DKH3OLTM4IXOQ2FM","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":"6714c6f1ca975462a3c168481ed55b6aa770112ccd8b1ef12d5603dc07a23556","cross_cats_sorted":["cs.AI","cs.GR","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-10T15:26:23Z","title_canon_sha256":"1828d83212f2e58fb0ed94a79b526ea0044243b740730942fd66aad63f61ff72"},"schema_version":"1.0","source":{"id":"1704.02906","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.02906","created_at":"2026-05-18T00:10:47Z"},{"alias_kind":"arxiv_version","alias_value":"1704.02906v3","created_at":"2026-05-18T00:10:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.02906","created_at":"2026-05-18T00:10:47Z"},{"alias_kind":"pith_short_12","alias_value":"I65C2IN3Q6DK","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"I65C2IN3Q6DKH3OL","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"I65C2IN3","created_at":"2026-05-18T12:31:21Z"}],"graph_snapshots":[{"event_id":"sha256:6714e7d9ac9aa5d7773b3b62a0b134a3f2b15a6f169f882ab2d6695b98a0c41c","target":"graph","created_at":"2026-05-18T00:10:47Z","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":"We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks (GANs) and its conditional variants to address the well known problem of mode collapse. First, MAD-GAN is a multi-agent GAN architecture incorporating multiple generators and one discriminator. Second, to enforce that different generators capture diverse high probability modes, the discriminator of MAD-GAN is designed such that along with finding the real and fake samples, it is also required to identify the generator that generated the given fake sample. Intuitively, to succeed in this task, the discriminat","authors_text":"Arnab Ghosh, Philip H. S. Torr, Puneet K. Dokania, Vinay Namboodiri, Viveka Kulharia","cross_cats":["cs.AI","cs.GR","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-10T15:26:23Z","title":"Multi-Agent Diverse Generative Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.02906","kind":"arxiv","version":3},"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:d18d31139080058df6570a87c7dbee5da1b563cd281bc443b956b4a4297effa7","target":"record","created_at":"2026-05-18T00:10:47Z","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":"6714c6f1ca975462a3c168481ed55b6aa770112ccd8b1ef12d5603dc07a23556","cross_cats_sorted":["cs.AI","cs.GR","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-10T15:26:23Z","title_canon_sha256":"1828d83212f2e58fb0ed94a79b526ea0044243b740730942fd66aad63f61ff72"},"schema_version":"1.0","source":{"id":"1704.02906","kind":"arxiv","version":3}},"canonical_sha256":"47ba2d21bb8786a3edcb9b388bba1a2b3f2d6fd98f6ff1db23ec4c528b01fac8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"47ba2d21bb8786a3edcb9b388bba1a2b3f2d6fd98f6ff1db23ec4c528b01fac8","first_computed_at":"2026-05-18T00:10:47.812116Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:10:47.812116Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1BTcxzngjd/5U+Z5McD2QsSncNN+15PmpBRYn0r4gTbCiLigVwFJ56QgnUXBHPxRoShOn/coG+bW3WcU16rOCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:10:47.812766Z","signed_message":"canonical_sha256_bytes"},"source_id":"1704.02906","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d18d31139080058df6570a87c7dbee5da1b563cd281bc443b956b4a4297effa7","sha256:6714e7d9ac9aa5d7773b3b62a0b134a3f2b15a6f169f882ab2d6695b98a0c41c"],"state_sha256":"b86c8672f4c9d9d06751f40d9de35aad07fccac1eb8360e2cef4a9a18e8fdf30"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ttboLKvNqZ9KNwEcuAI8P+2hjU34oEpS4g5PsmR2INYATXK89ErW3PLjfhVK4rYRiCayKLojb7eO3CkoK9IWAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T22:33:42.113455Z","bundle_sha256":"b889bad8f2304e77c47fe28f1940740467f487f1b1371be8ce4abf0627de8766"}}