{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:GXG3ZJM3TEBH77366ZQ5UFFRXL","short_pith_number":"pith:GXG3ZJM3","schema_version":"1.0","canonical_sha256":"35cdbca59b99027fff7ef661da14b1baedb9c076a18685712440c978c2655fd7","source":{"kind":"arxiv","id":"1312.4400","version":3},"attestation_state":"computed","paper":{"title":"Network In Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.NE","authors_text":"Min Lin, Qiang Chen, Shuicheng Yan","submitted_at":"2013-12-16T15:34:13Z","abstract_excerpt":"We propose a novel deep network structure called \"Network In Network\" (NIN) to enhance model discriminability for local patches within the receptive field. The conventional convolutional layer uses linear filters followed by a nonlinear activation function to scan the input. Instead, we build micro neural networks with more complex structures to abstract the data within the receptive field. We instantiate the micro neural network with a multilayer perceptron, which is a potent function approximator. The feature maps are obtained by sliding the micro networks over the input in a similar manner "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1312.4400","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2013-12-16T15:34:13Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"41640baa018b486e0fb45ffdea72586c46196b8db08fbb20f40b6d9f6c6598b0","abstract_canon_sha256":"07898da78c4b1a4289d23367e1ca5bd925ecad8a557e4623d5d7aa36a8e16944"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:57:17.028848Z","signature_b64":"GRBRhjoZFsoOi77Hcf+BEFhhmYwT4w4uMLx570xn6ZD24wC9e/3l66o43wIjRW+fa4V30eAczdCFqNSH3k8XBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35cdbca59b99027fff7ef661da14b1baedb9c076a18685712440c978c2655fd7","last_reissued_at":"2026-05-18T02:57:17.028333Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:57:17.028333Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Network In Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.NE","authors_text":"Min Lin, Qiang Chen, Shuicheng Yan","submitted_at":"2013-12-16T15:34:13Z","abstract_excerpt":"We propose a novel deep network structure called \"Network In Network\" (NIN) to enhance model discriminability for local patches within the receptive field. The conventional convolutional layer uses linear filters followed by a nonlinear activation function to scan the input. Instead, we build micro neural networks with more complex structures to abstract the data within the receptive field. We instantiate the micro neural network with a multilayer perceptron, which is a potent function approximator. The feature maps are obtained by sliding the micro networks over the input in a similar manner "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1312.4400","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1312.4400","created_at":"2026-05-18T02:57:17.028412+00:00"},{"alias_kind":"arxiv_version","alias_value":"1312.4400v3","created_at":"2026-05-18T02:57:17.028412+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1312.4400","created_at":"2026-05-18T02:57:17.028412+00:00"},{"alias_kind":"pith_short_12","alias_value":"GXG3ZJM3TEBH","created_at":"2026-05-18T12:27:46.883200+00:00"},{"alias_kind":"pith_short_16","alias_value":"GXG3ZJM3TEBH7736","created_at":"2026-05-18T12:27:46.883200+00:00"},{"alias_kind":"pith_short_8","alias_value":"GXG3ZJM3","created_at":"2026-05-18T12:27:46.883200+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2510.03516","citing_title":"COMET: Co-Optimization of a CNN Model using Efficient-Hardware OBC Techniques","ref_index":21,"is_internal_anchor":true},{"citing_arxiv_id":"2605.13887","citing_title":"Breaking Global Self-Attention Bottlenecks in Transformer-based Spiking Neural Networks with Local Structure-Aware Self-Attention","ref_index":37,"is_internal_anchor":true},{"citing_arxiv_id":"1605.07146","citing_title":"Wide Residual Networks","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"1510.00149","citing_title":"Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2604.27870","citing_title":"Parameter-Efficient Architectural Modifications for Translation-Invariant CNNs","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10546","citing_title":"Higher Resolution, Better Generalization: Unlocking Visual Scaling in Deep Reinforcement Learning","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00874","citing_title":"Latent Space Probing for Adult Content Detection in Video Generative Models","ref_index":56,"is_internal_anchor":false},{"citing_arxiv_id":"1512.03385","citing_title":"Deep Residual Learning for Image Recognition","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GXG3ZJM3TEBH77366ZQ5UFFRXL","json":"https://pith.science/pith/GXG3ZJM3TEBH77366ZQ5UFFRXL.json","graph_json":"https://pith.science/api/pith-number/GXG3ZJM3TEBH77366ZQ5UFFRXL/graph.json","events_json":"https://pith.science/api/pith-number/GXG3ZJM3TEBH77366ZQ5UFFRXL/events.json","paper":"https://pith.science/paper/GXG3ZJM3"},"agent_actions":{"view_html":"https://pith.science/pith/GXG3ZJM3TEBH77366ZQ5UFFRXL","download_json":"https://pith.science/pith/GXG3ZJM3TEBH77366ZQ5UFFRXL.json","view_paper":"https://pith.science/paper/GXG3ZJM3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1312.4400&json=true","fetch_graph":"https://pith.science/api/pith-number/GXG3ZJM3TEBH77366ZQ5UFFRXL/graph.json","fetch_events":"https://pith.science/api/pith-number/GXG3ZJM3TEBH77366ZQ5UFFRXL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GXG3ZJM3TEBH77366ZQ5UFFRXL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GXG3ZJM3TEBH77366ZQ5UFFRXL/action/storage_attestation","attest_author":"https://pith.science/pith/GXG3ZJM3TEBH77366ZQ5UFFRXL/action/author_attestation","sign_citation":"https://pith.science/pith/GXG3ZJM3TEBH77366ZQ5UFFRXL/action/citation_signature","submit_replication":"https://pith.science/pith/GXG3ZJM3TEBH77366ZQ5UFFRXL/action/replication_record"}},"created_at":"2026-05-18T02:57:17.028412+00:00","updated_at":"2026-05-18T02:57:17.028412+00:00"}