{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/towards-interpretable-deep-networks-for","title":"Towards Interpretable Deep Networks for Monocular Depth Estimation","arxiv_id":"2108.05312","date":"2021-08-11","proceeding":"ICCV 2021 10","authors":["Zunzhi You","Yi-Hsuan Tsai","Wei-Chen Chiu","Guanbin Li"],"abstract":"Deep networks for Monocular Depth Estimation (MDE) have achieved promising performance recently and it is of great importance to further understand the interpretability of these networks. Existing methods attempt to provide posthoc explanations by investigating visual cues, which may not explore the internal representations learned by deep networks. In this paper, we find that some hidden units of the network are selective to certain ranges of depth, and thus such behavior can be served as a way to interpret the internal representations. Based on our observations, we quantify the interpretability of a deep MDE network by the depth selectivity of its hidden units. Moreover, we then propose a method to train interpretable MDE deep networks without changing their original architectures, by assigning a depth range for each unit to select. Experimental results demonstrate that our method is able to enhance the interpretability of deep MDE networks by largely improving the depth selectivity of their units, while not harming or even improving the depth estimation accuracy. We further provide a comprehensive analysis to show the reliability of selective units, the applicability of our method on different layers, models, and datasets, and a demonstration on analysis of model error. Source code and models are available at https://github.com/youzunzhi/InterpretableMDE .","url_abs":"https://arxiv.org/abs/2108.05312v1","url_pdf":"https://arxiv.org/pdf/2108.05312v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"towards-interpretable-deep-networks-for","repo_url":"https://github.com/youzunzhi/interpretablemde","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"monocular-depth-estimation","task_name":"Monocular Depth Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2108.05312","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.05312"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/youzunzhi/InterpretableMDE","reach":null}],"summary":{"ran":5},"by_repo_kind":{"official":{"samples":5,"ran":5,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"a7351e4a2a751444","entry":"atrous_conv","repo":"youzunzhi/InterpretableMDE","repo_kind":"official","path":"model/bts/modules.py","file_url":"https://github.com/youzunzhi/InterpretableMDE/blob/HEAD/model/bts/modules.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a7351e4a2a751444"}},{"code_sha256_prefix":"8a49b510e671008e","entry":"bts","repo":"youzunzhi/InterpretableMDE","repo_kind":"official","path":"model/bts/modules.py","file_url":"https://github.com/youzunzhi/InterpretableMDE/blob/HEAD/model/bts/modules.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8a49b510e671008e"}},{"code_sha256_prefix":"238cbbeee2487336","entry":"local_planar_guidance","repo":"youzunzhi/InterpretableMDE","repo_kind":"official","path":"model/bts/modules.py","file_url":"https://github.com/youzunzhi/InterpretableMDE/blob/HEAD/model/bts/modules.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"238cbbeee2487336"}},{"code_sha256_prefix":"7a747dbea815af1b","entry":"reduction_1x1","repo":"youzunzhi/InterpretableMDE","repo_kind":"official","path":"model/bts/modules.py","file_url":"https://github.com/youzunzhi/InterpretableMDE/blob/HEAD/model/bts/modules.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7a747dbea815af1b"}},{"code_sha256_prefix":"49763904bc51bfe9","entry":"upconv","repo":"youzunzhi/InterpretableMDE","repo_kind":"official","path":"model/bts/modules.py","file_url":"https://github.com/youzunzhi/InterpretableMDE/blob/HEAD/model/bts/modules.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"49763904bc51bfe9"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}