{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:7ZWQIJ4XJLJWNPMD4WL3IMNHGH","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":"4696ababa367a30b6b9ee4af8c3efdf4f3e8da68f60c159d280471e05f88280f","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-14T14:33:26Z","title_canon_sha256":"4b9ba042d5fa7d566139a813df2014921105af90fa1cd984d60faea14a7db530"},"schema_version":"1.0","source":{"id":"1903.06048","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.06048","created_at":"2026-07-05T01:09:55Z"},{"alias_kind":"arxiv_version","alias_value":"1903.06048v4","created_at":"2026-07-05T01:09:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.06048","created_at":"2026-07-05T01:09:55Z"},{"alias_kind":"pith_short_12","alias_value":"7ZWQIJ4XJLJW","created_at":"2026-07-05T01:09:55Z"},{"alias_kind":"pith_short_16","alias_value":"7ZWQIJ4XJLJWNPMD","created_at":"2026-07-05T01:09:55Z"},{"alias_kind":"pith_short_8","alias_value":"7ZWQIJ4X","created_at":"2026-07-05T01:09:55Z"}],"graph_snapshots":[{"event_id":"sha256:11ed571836cbb495d0c226886a9cc98334cb615af55ac0783f64e590e52c41ce","target":"graph","created_at":"2026-07-05T01:09:55Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1903.06048/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While Generative Adversarial Networks (GANs) have seen huge successes in image synthesis tasks, they are notoriously difficult to adapt to different datasets, in part due to instability during training and sensitivity to hyperparameters. One commonly accepted reason for this instability is that gradients passing from the discriminator to the generator become uninformative when there isn't enough overlap in the supports of the real and fake distributions. In this work, we propose the Multi-Scale Gradient Generative Adversarial Network (MSG-GAN), a simple but effective technique for addressing t","authors_text":"Animesh Karnewar, Oliver Wang","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-14T14:33:26Z","title":"MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.06048","kind":"arxiv","version":4},"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:a21aecd6bc0fb7a575fe6747ad27fea0ea57a0fe6f017ea85a2cb3f623839b2f","target":"record","created_at":"2026-07-05T01:09:55Z","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":"4696ababa367a30b6b9ee4af8c3efdf4f3e8da68f60c159d280471e05f88280f","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-14T14:33:26Z","title_canon_sha256":"4b9ba042d5fa7d566139a813df2014921105af90fa1cd984d60faea14a7db530"},"schema_version":"1.0","source":{"id":"1903.06048","kind":"arxiv","version":4}},"canonical_sha256":"fe6d0427974ad366bd83e597b431a731cd969131f04f9170482ee73b4d228263","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fe6d0427974ad366bd83e597b431a731cd969131f04f9170482ee73b4d228263","first_computed_at":"2026-07-05T01:09:55.474755Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:09:55.474755Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rkEaxIrM5Yxm2Ui9BBVs3RgXJ7zMWtL5jrqLj9N1jpDhEgSuPiTeJk3bVnQy5NW93XKJmOT21+o8pbf2Qt+IAA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:09:55.475196Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.06048","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a21aecd6bc0fb7a575fe6747ad27fea0ea57a0fe6f017ea85a2cb3f623839b2f","sha256:11ed571836cbb495d0c226886a9cc98334cb615af55ac0783f64e590e52c41ce"],"state_sha256":"98c5740834ae99375bcda1576ff52f501bc9a7ba683ebba658e5d6d4b15efd37"}