{"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/lq-nets-learned-quantization-for-highly","title":"LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks","arxiv_id":"1807.10029","date":"2018-07-26","proceeding":"ECCV 2018 9","authors":["Dongqing Zhang","Jiaolong Yang","Dongqiangzi Ye","Gang Hua"],"abstract":"Although weight and activation quantization is an effective approach for Deep\nNeural Network (DNN) compression and has a lot of potentials to increase\ninference speed leveraging bit-operations, there is still a noticeable gap in\nterms of prediction accuracy between the quantized model and the full-precision\nmodel. To address this gap, we propose to jointly train a quantized,\nbit-operation-compatible DNN and its associated quantizers, as opposed to using\nfixed, handcrafted quantization schemes such as uniform or logarithmic\nquantization. Our method for learning the quantizers applies to both network\nweights and activations with arbitrary-bit precision, and our quantizers are\neasy to train. The comprehensive experiments on CIFAR-10 and ImageNet datasets\nshow that our method works consistently well for various network structures\nsuch as AlexNet, VGG-Net, GoogLeNet, ResNet, and DenseNet, surpassing previous\nquantization methods in terms of accuracy by an appreciable margin. Code\navailable at https://github.com/Microsoft/LQ-Nets","url_abs":"http://arxiv.org/abs/1807.10029v1","url_pdf":"http://arxiv.org/pdf/1807.10029v1.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":"lq-nets-learned-quantization-for-highly","repo_url":"https://github.com/Microsoft/LQ-Nets","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"quantization","task_name":"Quantization"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"auxiliary-classifier","method_name":"Auxiliary Classifier"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-block","method_name":"Dense Block"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"googlenet","method_name":"GoogLeNet"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"inception-module","method_name":"Inception Module"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"local-response-normalization","method_name":"Local Response Normalization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"speed","method_name":"SPEED"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.10029","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.10029"}},"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":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Microsoft/LQ-Nets","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":1},"by_repo_kind":{"official":{"samples":1,"ran":0,"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":"2847dfb0cae3736c","entry":"get_bn","repo":"Microsoft/LQ-Nets","repo_kind":"official","path":"resnet_model.py","file_url":"https://github.com/Microsoft/LQ-Nets/blob/HEAD/resnet_model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"2847dfb0cae3736c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}