{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:JKJUEFJSK3H7JBP5OOF5MPNQVH","short_pith_number":"pith:JKJUEFJS","canonical_record":{"source":{"id":"2103.08116","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-15T03:26:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0592ba710eb293edf43c6e9eb7e530ae9a7eb7b88bd5d2a60b40f4c4265ba1e8","abstract_canon_sha256":"0f62677594719bde3cfd3870c2f8220b7f37c2c0c7ff6de4a68e82758e02e006"},"schema_version":"1.0"},"canonical_sha256":"4a9342153256cff485fd738bd63db0a9d086e9fcb93f340191b5f294e7914e67","source":{"kind":"arxiv","id":"2103.08116","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.08116","created_at":"2026-07-05T03:16:51Z"},{"alias_kind":"arxiv_version","alias_value":"2103.08116v2","created_at":"2026-07-05T03:16:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.08116","created_at":"2026-07-05T03:16:51Z"},{"alias_kind":"pith_short_12","alias_value":"JKJUEFJSK3H7","created_at":"2026-07-05T03:16:51Z"},{"alias_kind":"pith_short_16","alias_value":"JKJUEFJSK3H7JBP5","created_at":"2026-07-05T03:16:51Z"},{"alias_kind":"pith_short_8","alias_value":"JKJUEFJS","created_at":"2026-07-05T03:16:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:JKJUEFJSK3H7JBP5OOF5MPNQVH","target":"record","payload":{"canonical_record":{"source":{"id":"2103.08116","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-15T03:26:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0592ba710eb293edf43c6e9eb7e530ae9a7eb7b88bd5d2a60b40f4c4265ba1e8","abstract_canon_sha256":"0f62677594719bde3cfd3870c2f8220b7f37c2c0c7ff6de4a68e82758e02e006"},"schema_version":"1.0"},"canonical_sha256":"4a9342153256cff485fd738bd63db0a9d086e9fcb93f340191b5f294e7914e67","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:16:51.243656Z","signature_b64":"C7In18tQ7DatqTeanvvH1koOk88aYS4O4x6yf2MipG1Cmm90oKEcVwAphqGk1OlDtJxKwmcLMtWG1XbyVS0jCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4a9342153256cff485fd738bd63db0a9d086e9fcb93f340191b5f294e7914e67","last_reissued_at":"2026-07-05T03:16:51.243175Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:16:51.243175Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2103.08116","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":"2026-07-05T03:16:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wuj+BJQctq5GCjNpg5cRBd47Tc2mv/Uyr482XTgRvH8pSz9kwlfdsvhjmUJMC7Avp7d+p7+Ve5++MhriViApCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T18:05:02.590456Z"},"content_sha256":"7d87e3d29c74f2fdc4a66cf058c15f5b2a2f14d25a6c6d6f0d019b5641d69e1b","schema_version":"1.0","event_id":"sha256:7d87e3d29c74f2fdc4a66cf058c15f5b2a2f14d25a6c6d6f0d019b5641d69e1b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:JKJUEFJSK3H7JBP5OOF5MPNQVH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving Generalization of Transfer Learning Across Domains Using Spatio-Temporal Features in Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Laura Zheng, Ming Lin, Shivam Akhauri, Tom Goldstein","submitted_at":"2021-03-15T03:26:06Z","abstract_excerpt":"Practical learning-based autonomous driving models must be capable of generalizing learned behaviors from simulated to real domains, and from training data to unseen domains with unusual image properties. In this paper, we investigate transfer learning methods that achieve robustness to domain shifts by taking advantage of the invariance of spatio-temporal features across domains. In this paper, we propose a transfer learning method to improve generalization across domains via transfer of spatio-temporal features and salient data augmentation. Our model uses a CNN-LSTM network with Inception m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.08116","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2103.08116/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T03:16:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wpCtQcXZR9YwAVb8iLt1AiZlJa1pbI4wLzs7bSOt5zc3XLztCxqhKtjR+nSKCA+R8DKuIKiIC0DeesQVVmDUCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T18:05:02.591099Z"},"content_sha256":"4b6277545e81a4397ca3b06cb359e2deb0adf0054b46151495d7f4024b12bcd2","schema_version":"1.0","event_id":"sha256:4b6277545e81a4397ca3b06cb359e2deb0adf0054b46151495d7f4024b12bcd2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JKJUEFJSK3H7JBP5OOF5MPNQVH/bundle.json","state_url":"https://pith.science/pith/JKJUEFJSK3H7JBP5OOF5MPNQVH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JKJUEFJSK3H7JBP5OOF5MPNQVH/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":"2026-08-09T18:05:02Z","links":{"resolver":"https://pith.science/pith/JKJUEFJSK3H7JBP5OOF5MPNQVH","bundle":"https://pith.science/pith/JKJUEFJSK3H7JBP5OOF5MPNQVH/bundle.json","state":"https://pith.science/pith/JKJUEFJSK3H7JBP5OOF5MPNQVH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JKJUEFJSK3H7JBP5OOF5MPNQVH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:JKJUEFJSK3H7JBP5OOF5MPNQVH","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":"0f62677594719bde3cfd3870c2f8220b7f37c2c0c7ff6de4a68e82758e02e006","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-15T03:26:06Z","title_canon_sha256":"0592ba710eb293edf43c6e9eb7e530ae9a7eb7b88bd5d2a60b40f4c4265ba1e8"},"schema_version":"1.0","source":{"id":"2103.08116","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.08116","created_at":"2026-07-05T03:16:51Z"},{"alias_kind":"arxiv_version","alias_value":"2103.08116v2","created_at":"2026-07-05T03:16:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.08116","created_at":"2026-07-05T03:16:51Z"},{"alias_kind":"pith_short_12","alias_value":"JKJUEFJSK3H7","created_at":"2026-07-05T03:16:51Z"},{"alias_kind":"pith_short_16","alias_value":"JKJUEFJSK3H7JBP5","created_at":"2026-07-05T03:16:51Z"},{"alias_kind":"pith_short_8","alias_value":"JKJUEFJS","created_at":"2026-07-05T03:16:51Z"}],"graph_snapshots":[{"event_id":"sha256:4b6277545e81a4397ca3b06cb359e2deb0adf0054b46151495d7f4024b12bcd2","target":"graph","created_at":"2026-07-05T03:16:51Z","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/2103.08116/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Practical learning-based autonomous driving models must be capable of generalizing learned behaviors from simulated to real domains, and from training data to unseen domains with unusual image properties. In this paper, we investigate transfer learning methods that achieve robustness to domain shifts by taking advantage of the invariance of spatio-temporal features across domains. In this paper, we propose a transfer learning method to improve generalization across domains via transfer of spatio-temporal features and salient data augmentation. Our model uses a CNN-LSTM network with Inception m","authors_text":"Laura Zheng, Ming Lin, Shivam Akhauri, Tom Goldstein","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-15T03:26:06Z","title":"Improving Generalization of Transfer Learning Across Domains Using Spatio-Temporal Features in Autonomous Driving"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.08116","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":"sha256:7d87e3d29c74f2fdc4a66cf058c15f5b2a2f14d25a6c6d6f0d019b5641d69e1b","target":"record","created_at":"2026-07-05T03:16:51Z","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":"0f62677594719bde3cfd3870c2f8220b7f37c2c0c7ff6de4a68e82758e02e006","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-15T03:26:06Z","title_canon_sha256":"0592ba710eb293edf43c6e9eb7e530ae9a7eb7b88bd5d2a60b40f4c4265ba1e8"},"schema_version":"1.0","source":{"id":"2103.08116","kind":"arxiv","version":2}},"canonical_sha256":"4a9342153256cff485fd738bd63db0a9d086e9fcb93f340191b5f294e7914e67","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4a9342153256cff485fd738bd63db0a9d086e9fcb93f340191b5f294e7914e67","first_computed_at":"2026-07-05T03:16:51.243175Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:16:51.243175Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"C7In18tQ7DatqTeanvvH1koOk88aYS4O4x6yf2MipG1Cmm90oKEcVwAphqGk1OlDtJxKwmcLMtWG1XbyVS0jCg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:16:51.243656Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.08116","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7d87e3d29c74f2fdc4a66cf058c15f5b2a2f14d25a6c6d6f0d019b5641d69e1b","sha256:4b6277545e81a4397ca3b06cb359e2deb0adf0054b46151495d7f4024b12bcd2"],"state_sha256":"aa7170459c4aaaf0cf8e33841053c8c5fb2cdf56257efdd6a8990e4d03204aa2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9uRgMxoHo1eox6iAEsfCtWY73g8N1eAq5BJjj4EQ7hYm9SV0B3gdJTETQbrPB0vy0GpxMno4HzWAMfsJXlIYAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T18:05:02.597380Z","bundle_sha256":"3fb72ba535088ce601c33db94272cfb4f8e0778559fe758d0bb2e369307f8566"}}