{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/auxiliary-classifier/papers/3","list_of":"/method/auxiliary-classifier","method":"Auxiliary Classifier","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":3,"pages_in_order":4,"rows_per_page":100,"rows":[201,300],"of":395,"counts":{"archive_papers_tagged":395,"with_a_code_link":145,"where_syntology_ran_a_sample":36,"not_listed_spam_title":0,"listed":395,"listed_where_code_ran":36,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":28,"every_run_a_failure_of_syntologys_instrument":8,"listed_with_a_run_with_no_instrument_failure":28,"listed_every_run_a_failure_of_syntologys_instrument":8,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/auxiliary-classifier","prev":"/method/auxiliary-classifier/papers/2","next":"/method/auxiliary-classifier/papers/4","papers":[{"paper":"/paper/a-novel-and-reliable-deep-learning-web-based","slug":"a-novel-and-reliable-deep-learning-web-based","title":"A Novel and Reliable Deep Learning Web-Based Tool to Detect COVID-19 Infection from Chest CT-Scan","date":"2020-06-24","arxiv_id":"2006.14419","n_code_links":1,"syntology":null},{"paper":"/paper/intriguing-generalization-and-simplicity-of","slug":"intriguing-generalization-and-simplicity-of","title":"The shape and simplicity biases of adversarially robust ImageNet-trained CNNs","date":"2020-06-16","arxiv_id":"2006.09373","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":null}},{"paper":null,"slug":"multi-precision-policy-enforced-training-1","title":"Multi-Precision Policy Enforced Training (MuPPET): A precision-switching strategy for quantised fixed-point training of CNNs","date":"2020-06-16","arxiv_id":"2006.09049","n_code_links":0,"syntology":null},{"paper":null,"slug":"real-time-universal-style-transfer-on-high","title":"Real-time Universal Style Transfer on High-resolution Images via Zero-channel Pruning","date":"2020-06-16","arxiv_id":"2006.09029","n_code_links":0,"syntology":null},{"paper":"/paper/unbiased-auxiliary-classifier-gans-with-mine","slug":"unbiased-auxiliary-classifier-gans-with-mine","title":"Unbiased Auxiliary Classifier GANs with MINE","date":"2020-06-13","arxiv_id":"2006.07567","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["phymhan/ACGAN-PyTorch"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"evaluation-of-deep-segmentation-models-for","title":"Exploiting the Transferability of Deep Learning Systems Across Multi-modal Retinal Scans for Extracting Retinopathy Lesions","date":"2020-06-04","arxiv_id":"2006.02662","n_code_links":0,"syntology":null},{"paper":null,"slug":"bwcnn-blink-to-word-a-real-time-convolutional","title":"BWCNN: Blink to Word, a Real-Time Convolutional Neural Network Approach","date":"2020-06-01","arxiv_id":"2006.01232","n_code_links":0,"syntology":null},{"paper":null,"slug":"sdct-auxnet-th-dct-augmented-stain","title":"SDCT-AuxNet$^θ$: DCT Augmented Stain Deconvolutional CNN with Auxiliary Classifier for Cancer Diagnosis","date":"2020-05-30","arxiv_id":"2006.00304","n_code_links":0,"syntology":null},{"paper":null,"slug":"brief-announcement-on-the-limits-of","title":"Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs","date":"2020-05-28","arxiv_id":"2005.13823","n_code_links":0,"syntology":null},{"paper":null,"slug":"coronavirus-comparing-covid-19-sars-and-mers","title":"Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-Ray Images","date":"2020-05-23","arxiv_id":"2005.11524","n_code_links":0,"syntology":null},{"paper":null,"slug":"synthesizing-unrestricted-false-positive","title":"Synthesizing Unrestricted False Positive Adversarial Objects Using Generative Models","date":"2020-05-19","arxiv_id":"2005.09294","n_code_links":0,"syntology":null},{"paper":"/paper/learning-generalized-spoof-cues-for-face-anti","slug":"learning-generalized-spoof-cues-for-face-anti","title":"Learning Generalized Spoof Cues for Face Anti-spoofing","date":"2020-05-08","arxiv_id":"2005.03922","n_code_links":6,"syntology":null},{"paper":null,"slug":"comparison-and-benchmarking-of-ai-models-and","title":"Comparison and Benchmarking of AI Models and Frameworks on Mobile Devices","date":"2020-05-07","arxiv_id":"2005.05085","n_code_links":0,"syntology":null},{"paper":null,"slug":"3d-printed-brain-controlled-robot-arm","title":"3D Printed Brain-Controlled Robot-Arm Prosthetic via Embedded Deep Learning from sEMG Sensors","date":"2020-05-04","arxiv_id":"2005.01797","n_code_links":0,"syntology":null},{"paper":"/paper/on-the-safety-of-vulnerable-road-users-by","slug":"on-the-safety-of-vulnerable-road-users-by","title":"On the safety of vulnerable road users by cyclist orientation detection using Deep Learning","date":"2020-04-25","arxiv_id":"2004.11909","n_code_links":0,"syntology":null},{"paper":"/paper/automated-diagnosis-of-covid-19-with-limited","slug":"automated-diagnosis-of-covid-19-with-limited","title":"Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks","date":"2020-04-23","arxiv_id":"2004.11676","n_code_links":1,"syntology":null},{"paper":null,"slug":"detection-of-covid-19-from-chest-x-ray-images","title":"Detection of Covid-19 From Chest X-ray Images Using Artificial Intelligence: An Early Review","date":"2020-04-11","arxiv_id":"2004.05436","n_code_links":0,"syntology":null},{"paper":null,"slug":"fully-automatic-electrocardiogram","title":"Fully Automatic Electrocardiogram Classification System based on Generative Adversarial Network with Auxiliary Classifier","date":"2020-04-10","arxiv_id":"2004.04894","n_code_links":0,"syntology":null},{"paper":null,"slug":"gsa-densenet121-covid-19-a-hybrid-deep","title":"GSA-DenseNet121-COVID-19: a Hybrid Deep Learning Architecture for the Diagnosis of COVID-19 Disease based on Gravitational Search Optimization Algorithm","date":"2020-04-09","arxiv_id":"2004.05084","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-deep-learning-approach-for-determining","title":"A Deep Learning Approach for Determining Effects of Tuta Absoluta in Tomato Plants","date":"2020-04-08","arxiv_id":"2004.04023","n_code_links":0,"syntology":null},{"paper":null,"slug":"skin-diseases-detection-using-lbp-and-wld-an","title":"Skin Diseases Detection using LBP and WLD- An Ensembling Approach","date":"2020-04-08","arxiv_id":"2004.04122","n_code_links":0,"syntology":null},{"paper":null,"slug":"within-the-lack-of-covid-19-benchmark-dataset","title":"Within the Lack of COVID-19 Benchmark Dataset: A Novel GAN with Deep Transfer Learning for Corona-virus Detection in Chest X-ray Images","date":"2020-04-07","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-of-coronavirus-covid-19-associated","title":"Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset","date":"2020-04-02","arxiv_id":"2004.01184","n_code_links":0,"syntology":null},{"paper":"/paper/learning-from-small-data-through-sampling-an","slug":"learning-from-small-data-through-sampling-an","title":"Generative Latent Implicit Conditional Optimization when Learning from Small Sample","date":"2020-03-31","arxiv_id":"2003.14297","n_code_links":1,"syntology":{"ran":10,"of":16,"n_ran_checked":10,"n_instrument":0,"unverified":6,"pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","official":{"repos":["IdanAzuri/glico-learning-small-sample"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"classification-of-the-chinese-handwritten","title":"Classification of Chinese Handwritten Numbers with Labeled Projective Dictionary Pair Learning","date":"2020-03-26","arxiv_id":"2003.11700","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-new-multiple-max-pooling-integration-module","title":"A New Multiple Max-pooling Integration Module and Cross Multiscale Deconvolution Network Based on Image Semantic Segmentation","date":"2020-03-25","arxiv_id":"2003.11213","n_code_links":0,"syntology":null},{"paper":null,"slug":"performance-evaluation-of-advanced-deep","title":"Performance Evaluation of Advanced Deep Learning Architectures for Offline Handwritten Character Recognition","date":"2020-03-15","arxiv_id":"2003.06794","n_code_links":0,"syntology":null},{"paper":null,"slug":"identifying-individual-dogs-in-social-media","title":"Identifying Individual Dogs in Social Media Images","date":"2020-03-14","arxiv_id":"2003.06705","n_code_links":0,"syntology":null},{"paper":null,"slug":"advanced-deep-learning-methodologies-for-skin","title":"Advanced Deep Learning Methodologies for Skin Cancer Classification in Prodromal Stages","date":"2020-03-13","arxiv_id":"2003.06356","n_code_links":0,"syntology":null},{"paper":null,"slug":"syncgan-using-learnable-class-specific-priors","title":"SynCGAN: Using learnable class specific priors to generate synthetic data for improving classifier performance on cytological images","date":"2020-03-12","arxiv_id":"2003.05712","n_code_links":0,"syntology":null},{"paper":null,"slug":"traffic-signs-detection-and-recognition","title":"Traffic Signs Detection and Recognition System using Deep Learning","date":"2020-03-06","arxiv_id":"2003.03256","n_code_links":0,"syntology":null},{"paper":"/paper/neuron-shapley-discovering-the-responsible","slug":"neuron-shapley-discovering-the-responsible","title":"Neuron Shapley: Discovering the Responsible Neurons","date":"2020-02-23","arxiv_id":"2002.09815","n_code_links":1,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"0 ran · 2 unverified","official":{"repos":["amiratag/neuronshapley"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":null,"slug":"introducing-fuzzy-layers-for-deep-learning","title":"Introducing Fuzzy Layers for Deep Learning","date":"2020-02-21","arxiv_id":"2003.00880","n_code_links":0,"syntology":null},{"paper":"/paper/skip-connections-matter-on-the","slug":"skip-connections-matter-on-the","title":"Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets","date":"2020-02-14","arxiv_id":"2002.05990","n_code_links":4,"syntology":{"ran":4,"of":9,"n_ran_checked":1,"n_instrument":3,"unverified":5,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","official":{"repos":["csdongxian/skip-connections-matter"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"exponential-discretization-of-weights-of","title":"Exponential discretization of weights of neural network connections in pre-trained neural networks","date":"2020-02-03","arxiv_id":"2002.00623","n_code_links":0,"syntology":null},{"paper":null,"slug":"age-conditioned-synthesis-of-pediatric","title":"Age-Conditioned Synthesis of Pediatric Computed Tomography with Auxiliary Classifier Generative Adversarial Networks","date":"2020-01-31","arxiv_id":"2002.00011","n_code_links":0,"syntology":null},{"paper":null,"slug":"rpr-random-partition-relaxation-for-training","title":"RPR: Random Partition Relaxation for Training; Binary and Ternary Weight Neural Networks","date":"2020-01-04","arxiv_id":"2001.01091","n_code_links":0,"syntology":null},{"paper":"/paper/zeroq-a-novel-zero-shot-quantization","slug":"zeroq-a-novel-zero-shot-quantization","title":"ZeroQ: A Novel Zero Shot Quantization Framework","date":"2020-01-01","arxiv_id":"2001.00281","n_code_links":3,"syntology":{"ran":7,"of":19,"n_ran_checked":4,"n_instrument":3,"unverified":12,"pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 12 unverified","official":{"repos":["amirgholami/ZeroQ"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/topoact-exploring-the-shape-of-activations-in","slug":"topoact-exploring-the-shape-of-activations-in","title":"TopoAct: Visually Exploring the Shape of Activations in Deep Learning","date":"2019-12-13","arxiv_id":"1912.06332","n_code_links":1,"syntology":null},{"paper":"/paper/linear-mode-connectivity-and-the-lottery","slug":"linear-mode-connectivity-and-the-lottery","title":"Linear Mode Connectivity and the Lottery Ticket Hypothesis","date":"2019-12-11","arxiv_id":"1912.05671","n_code_links":2,"syntology":null},{"paper":"/paper/scratch-that-an-evolution-based-adversarial","slug":"scratch-that-an-evolution-based-adversarial","title":"Scratch that! An Evolution-based Adversarial Attack against Neural Networks","date":"2019-12-05","arxiv_id":"1912.02316","n_code_links":1,"syntology":null},{"paper":"/paper/twin-auxilary-classifiers-gan","slug":"twin-auxilary-classifiers-gan","title":"Twin Auxilary Classifiers GAN","date":"2019-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/filter-response-normalization-layer","slug":"filter-response-normalization-layer","title":"Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks","date":"2019-11-21","arxiv_id":"1911.09737","n_code_links":16,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"ai-based-pilgrim-detection-using","title":"AI-based Pilgrim Detection using Convolutional Neural Networks","date":"2019-11-18","arxiv_id":"1911.07509","n_code_links":0,"syntology":null},{"paper":"/paper/segmentation-guided-attention-network-for","slug":"segmentation-guided-attention-network-for","title":"Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss","date":"2019-11-18","arxiv_id":"1911.07990","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficacy-of-pixel-level-ood-detection-for-1","title":"Efficacy of Pixel-Level OOD Detection for Semantic Segmentation","date":"2019-11-07","arxiv_id":"1911.02897","n_code_links":0,"syntology":null},{"paper":null,"slug":"predictive-modeling-of-brain-tumor-a-deep","title":"Predictive modeling of brain tumor: A Deep learning approach","date":"2019-11-06","arxiv_id":"1911.02265","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-scalable-multilabel-classification-to","title":"A Scalable Multilabel Classification to Deploy Deep Learning Architectures For Edge Devices","date":"2019-11-05","arxiv_id":"1911.02098","n_code_links":0,"syntology":null},{"paper":null,"slug":"temporal-action-localization-using-long-short","title":"Temporal Action Localization using Long Short-Term Dependency","date":"2019-11-04","arxiv_id":"1911.01060","n_code_links":0,"syntology":null},{"paper":"/paper/similarity-based-auxiliary-classifier-for","slug":"similarity-based-auxiliary-classifier-for","title":"Similarity Based Auxiliary Classifier for Named Entity Recognition","date":"2019-11-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-and-control-algorithms-of","title":"Deep Learning and Control Algorithms of Direct Perception for Autonomous Driving","date":"2019-10-26","arxiv_id":"1910.12031","n_code_links":0,"syntology":null},{"paper":null,"slug":"establishing-an-evaluation-metric-to-quantify","title":"Establishing an Evaluation Metric to Quantify Climate Change Image Realism","date":"2019-10-22","arxiv_id":"1910.10143","n_code_links":0,"syntology":null},{"paper":"/paper/improving-sample-diversity-of-a-pre-trained","slug":"improving-sample-diversity-of-a-pre-trained","title":"A cost-effective method for improving and re-purposing large, pre-trained GANs by fine-tuning their class-embeddings","date":"2019-10-10","arxiv_id":"1910.04760","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-conditional-generative-model-for-predicting","title":"A Conditional Generative Model for Predicting Material Microstructures from Processing Methods","date":"2019-10-04","arxiv_id":"1910.02133","n_code_links":0,"syntology":null},{"paper":null,"slug":"dsconv-efficient-convolution-operator-1","title":"DSConv: Efficient Convolution Operator","date":"2019-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"gdp-generalized-device-placement-for-dataflow","title":"GDP: Generalized Device Placement for Dataflow Graphs","date":"2019-09-28","arxiv_id":"1910.01578","n_code_links":0,"syntology":null},{"paper":null,"slug":"190910227","title":"Deep Convolutions for In-Depth Automated Rock Typing","date":"2019-09-23","arxiv_id":"1909.10227","n_code_links":0,"syntology":null},{"paper":"/paper/out-of-domain-detection-for-natural-language","slug":"out-of-domain-detection-for-natural-language","title":"Out-of-domain Detection for Natural Language Understanding in Dialog Systems","date":"2019-09-09","arxiv_id":"1909.03862","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-auxiliary-classifier-generative","title":"An Auxiliary Classifier Generative Adversarial Framework for Relation Extraction","date":"2019-09-06","arxiv_id":"1909.05370","n_code_links":0,"syntology":null},{"paper":"/paper/inception-inspired-lstm-for-next-frame-video","slug":"inception-inspired-lstm-for-next-frame-video","title":"Inception-inspired LSTM for Next-frame Video Prediction","date":"2019-08-28","arxiv_id":"1909.05622","n_code_links":2,"syntology":null},{"paper":"/paper/tag2pix-line-art-colorization-using-text-tag","slug":"tag2pix-line-art-colorization-using-text-tag","title":"Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss","date":"2019-08-16","arxiv_id":"1908.05840","n_code_links":2,"syntology":null},{"paper":null,"slug":"histographs-graphs-in-histopathology","title":"Histographs: Graphs in Histopathology","date":"2019-08-14","arxiv_id":"1908.05020","n_code_links":0,"syntology":null},{"paper":null,"slug":"fine-tuning-models-comparisons-on-garbage","title":"Fine-Tuning Models Comparisons on Garbage Classification for Recyclability","date":"2019-08-07","arxiv_id":"1908.04393","n_code_links":0,"syntology":null},{"paper":null,"slug":"genetic-deep-learning-for-lung-cancer","title":"Genetic Deep Learning for Lung Cancer Screening","date":"2019-07-27","arxiv_id":"1907.11849","n_code_links":0,"syntology":null},{"paper":"/paper/twin-auxiliary-classifiers-gan","slug":"twin-auxiliary-classifiers-gan","title":"Twin Auxiliary Classifiers GAN","date":"2019-07-05","arxiv_id":"1907.02690","n_code_links":4,"syntology":null},{"paper":"/paper/mimic-and-fool-a-task-agnostic-adversarial","slug":"mimic-and-fool-a-task-agnostic-adversarial","title":"Mimic and Fool: A Task Agnostic Adversarial Attack","date":"2019-06-11","arxiv_id":"1906.04606","n_code_links":1,"syntology":null},{"paper":"/paper/reproduction-study-using-public-data-of","slug":"reproduction-study-using-public-data-of","title":"Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","date":"2019-06-06","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/deepshift-towards-multiplication-less-neural","slug":"deepshift-towards-multiplication-less-neural","title":"DeepShift: Towards Multiplication-Less Neural Networks","date":"2019-05-30","arxiv_id":"1905.13298","n_code_links":1,"syntology":null},{"paper":"/paper/transfer-learning-based-detection-of-diabetic","slug":"transfer-learning-based-detection-of-diabetic","title":"Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset","date":"2019-05-17","arxiv_id":"1905.07203","n_code_links":1,"syntology":null},{"paper":null,"slug":"self-supervised-audio-spatialization-with","title":"Self-supervised Audio Spatialization with Correspondence Classifier","date":"2019-05-14","arxiv_id":"1905.05375","n_code_links":0,"syntology":null},{"paper":null,"slug":"adversarially-trained-autoencoders-for","title":"Adversarially Trained Autoencoders for Parallel-Data-Free Voice Conversion","date":"2019-05-09","arxiv_id":"1905.03864","n_code_links":0,"syntology":null},{"paper":null,"slug":"double-transfer-learning-for-breast-cancer","title":"Double Transfer Learning for Breast Cancer Histopathologic Image Classification","date":"2019-04-16","arxiv_id":"1904.07834","n_code_links":0,"syntology":null},{"paper":"/paper/c3ae-exploring-the-limits-of-compact-model","slug":"c3ae-exploring-the-limits-of-compact-model","title":"C3AE: Exploring the Limits of Compact Model for Age Estimation","date":"2019-04-10","arxiv_id":"1904.05059","n_code_links":1,"syntology":null},{"paper":null,"slug":"unsupervised-feature-learning-for","title":"Unsupervised Feature Learning for Environmental Sound Classification Using Weighted Cycle-Consistent Generative Adversarial Network","date":"2019-04-08","arxiv_id":"1904.04221","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-end-to-end-baseline-for-video-captioning","title":"End-to-End Video Captioning","date":"2019-04-04","arxiv_id":"1904.02628","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-cnns-on-the-gestalt-principle-of","title":"Evaluating CNNs on the Gestalt Principle of Closure","date":"2019-03-30","arxiv_id":"1904.00285","n_code_links":0,"syntology":null},{"paper":null,"slug":"understanding-unconventional-preprocessors-in","title":"Understanding Unconventional Preprocessors in Deep Convolutional Neural Networks for Face Identification","date":"2019-03-27","arxiv_id":"1904.00815","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-enables-automatic-detection-and","title":"Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI","date":"2019-03-18","arxiv_id":"1903.07988","n_code_links":0,"syntology":null},{"paper":"/paper/image-privacy-prediction-using-deep-neural","slug":"image-privacy-prediction-using-deep-neural","title":"Image Privacy Prediction Using Deep Neural Networks","date":"2019-03-08","arxiv_id":"1903.03695","n_code_links":1,"syntology":null},{"paper":null,"slug":"long-and-short-memory-balancing-in-visual-co","title":"Long and Short Memory Balancing in Visual Co-Tracking using Q-Learning","date":"2019-02-14","arxiv_id":"1902.05211","n_code_links":0,"syntology":null},{"paper":null,"slug":"micik-mining-cross-layer-inherent-similarity","title":"MICIK: MIning Cross-Layer Inherent Similarity Knowledge for Deep Model Compression","date":"2019-02-03","arxiv_id":"1902.00918","n_code_links":0,"syntology":null},{"paper":null,"slug":"tunet-incorporating-segmentation-maps-to","title":"TUNet: Incorporating segmentation maps to improve classification","date":"2019-01-27","arxiv_id":"1901.11379","n_code_links":0,"syntology":null},{"paper":"/paper/vision-based-inspection-system-employing","slug":"vision-based-inspection-system-employing","title":"Vision-based inspection system employing computer vision & neural networks for detection of fractures in manufactured components","date":"2019-01-25","arxiv_id":"1901.08864","n_code_links":1,"syntology":null},{"paper":"/paper/towards-compact-convnets-via-structure","slug":"towards-compact-convnets-via-structure","title":"Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning","date":"2019-01-23","arxiv_id":"1901.07827","n_code_links":1,"syntology":null},{"paper":"/paper/automated-deep-photo-style-transfer","slug":"automated-deep-photo-style-transfer","title":"Automated Deep Photo Style Transfer","date":"2019-01-12","arxiv_id":"1901.03915","n_code_links":1,"syntology":null},{"paper":"/paper/dsconv-efficient-convolution-operator","slug":"dsconv-efficient-convolution-operator","title":"DSConv: Efficient Convolution Operator","date":"2019-01-07","arxiv_id":"1901.01928","n_code_links":1,"syntology":null},{"paper":"/paper/impact-of-ground-truth-annotation-quality-on","slug":"impact-of-ground-truth-annotation-quality-on","title":"Impact of Ground Truth Annotation Quality on Performance of Semantic Image Segmentation of Traffic Conditions","date":"2018-12-30","arxiv_id":"1901.00001","n_code_links":1,"syntology":null},{"paper":null,"slug":"fast-mvae-joint-separation-and-classification","title":"Fast MVAE: Joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier","date":"2018-12-16","arxiv_id":"1812.06391","n_code_links":0,"syntology":null},{"paper":"/paper/deepcalib-a-deep-learning-approach-for","slug":"deepcalib-a-deep-learning-approach-for","title":"DeepCalib: a deep learning approach for automatic intrinsic calibration of wide field-of-view cameras","date":"2018-12-15","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"accelerating-convolutional-neural-networks","title":"Accelerating Convolutional Neural Networks via Activation Map Compression","date":"2018-12-10","arxiv_id":"1812.04056","n_code_links":0,"syntology":null},{"paper":"/paper/strike-with-a-pose-neural-networks-are-easily","slug":"strike-with-a-pose-neural-networks-are-easily","title":"Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects","date":"2018-11-28","arxiv_id":"1811.11553","n_code_links":1,"syntology":null},{"paper":"/paper/class-distinct-and-class-mutual-image","slug":"class-distinct-and-class-mutual-image","title":"Class-Distinct and Class-Mutual Image Generation with GANs","date":"2018-11-27","arxiv_id":"1811.11163","n_code_links":2,"syntology":null},{"paper":"/paper/label-noise-robust-generative-adversarial","slug":"label-noise-robust-generative-adversarial","title":"Label-Noise Robust Generative Adversarial Networks","date":"2018-11-27","arxiv_id":"1811.11165","n_code_links":3,"syntology":{"ran":5,"of":6,"n_ran_checked":5,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["takuhirok/rGAN"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/multi-path-segmentation-network","slug":"multi-path-segmentation-network","title":"ShelfNet for Fast Semantic Segmentation","date":"2018-11-27","arxiv_id":"1811.11254","n_code_links":6,"syntology":null},{"paper":"/paper/context-aware-crowd-counting","slug":"context-aware-crowd-counting","title":"Context-Aware Crowd Counting","date":"2018-11-26","arxiv_id":"1811.10452","n_code_links":3,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["weizheliu/Context-Aware-Crowd-Counting"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/artificial-color-constancy-via-googlenet-with","slug":"artificial-color-constancy-via-googlenet-with","title":"Artificial Color Constancy via GoogLeNet with Angular Loss Function","date":"2018-11-20","arxiv_id":"1811.08456","n_code_links":1,"syntology":null},{"paper":null,"slug":"structure-learning-of-deep-neural-networks","title":"Structure Learning of Deep Neural Networks with Q-Learning","date":"2018-10-31","arxiv_id":"1810.13155","n_code_links":0,"syntology":null},{"paper":null,"slug":"fine-grained-classification-of-cervical-cells","title":"Fine-Grained Classification of Cervical Cells Using Morphological and Appearance Based Convolutional Neural Networks","date":"2018-10-14","arxiv_id":"1810.06058","n_code_links":0,"syntology":null},{"paper":"/paper/deepweeds-a-multiclass-weed-species-image","slug":"deepweeds-a-multiclass-weed-species-image","title":"DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning","date":"2018-10-09","arxiv_id":"1810.05726","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["AlexOlsen/DeepWeeds"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/rccnet-an-efficient-convolutional-neural","slug":"rccnet-an-efficient-convolutional-neural","title":"RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification","date":"2018-09-30","arxiv_id":"1810.02797","n_code_links":1,"syntology":null}],"record_sha256":"dfbadc327ec3696c01956ca9e504a2836e6060df823d34e27b21e6b0f1002adf","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}