{"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":"/task/small-data/papers/2","list_of":"/task/small-data","task":"Small Data Image Classification","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":2,"rows_per_page":100,"rows":[101,179],"of":179,"counts":{"archive_papers_tagged":179,"with_a_code_link":58,"where_syntology_ran_a_sample":15,"not_listed_spam_title":0,"listed":179,"listed_where_code_ran":15,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":13,"every_run_a_failure_of_syntologys_instrument":2,"listed_with_a_run_with_no_instrument_failure":13,"listed_every_run_a_failure_of_syntologys_instrument":2,"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":"/task/small-data","prev":"/task/small-data","next":null,"papers":[{"url":null,"slug":"self-supervised-learning-for-cardiac-mr-image","title":"Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction","date":"2019-07-05","arxiv_id":"1907.02757","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-inverse-regression-for-supervised","title":"Bayesian inverse regression for dimension reduction with small datasets","date":"2019-06-19","arxiv_id":"1906.08018","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-database-for-face-presentation-attack-using","title":"A database for face presentation attack using wax figure faces","date":"2019-06-06","arxiv_id":"1906.11900","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-learning-for-neural-dependency","title":"Bayesian Learning for Neural Dependency Parsing","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-simple-model-predictions-for","title":"Enhancing Simple Models by Exploiting What They Already Know","date":"2019-05-30","arxiv_id":"1905.13565","repositories_listed":0,"syntology":null},{"url":null,"slug":"scann-synthesis-of-compact-and-accurate","title":"SCANN: Synthesis of Compact and Accurate Neural Networks","date":"2019-04-19","arxiv_id":"1904.09090","repositories_listed":0,"syntology":null},{"url":null,"slug":"small-data-challenges-in-big-data-era-a","title":"Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods","date":"2019-03-27","arxiv_id":"1903.11260","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-we-still-need-fuzzy-classifiers-for-small","title":"Do we still need fuzzy classifiers for Small Data in the Era of Big Data?","date":"2019-03-08","arxiv_id":"1903.03324","repositories_listed":0,"syntology":null},{"url":null,"slug":"novel-quantitative-indicators-of-digital","title":"Novel quantitative indicators of digital ophthalmoscopy image quality","date":"2019-03-07","arxiv_id":"1903.02695","repositories_listed":0,"syntology":null},{"url":null,"slug":"lung-ct-imaging-sign-classification-through","title":"Lung CT Imaging Sign Classification through Deep Learning on Small Data","date":"2019-03-01","arxiv_id":"1903.00183","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-kernel-learning-from-u-statistics-of","title":"A Distributionally Robust Optimization Method for Adversarial Multiple Kernel Learning","date":"2019-02-27","arxiv_id":"1902.10365","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-in-the-loop-active-covariance-learning","title":"Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets","date":"2019-02-26","arxiv_id":"1902.09834","repositories_listed":0,"syntology":null},{"url":null,"slug":"pretrained-language-model-transfer-on-neural","title":"Pretrained language model transfer on neural named entity recognition in Indonesian conversational texts","date":"2019-02-21","arxiv_id":"1902.07938","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-hyperparameter-tuning-using-bayesian","title":"Fast Hyperparameter Tuning using Bayesian Optimization with Directional Derivatives","date":"2019-02-06","arxiv_id":"1902.02416","repositories_listed":0,"syntology":null},{"url":null,"slug":"avp-physics-informed-data-generation-for","title":"Active Image Synthesis for Efficient Labeling","date":"2019-02-05","arxiv_id":"1902.01522","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-feature-fusion-and-its-application-to","title":"Visual Feature Fusion and its Application to Support Unsupervised Clustering Tasks","date":"2019-01-16","arxiv_id":"1901.05556","repositories_listed":0,"syntology":null},{"url":null,"slug":"fully-automatic-segmentation-of-sublingual","title":"Fully Automatic Segmentation of Sublingual Veins from Retrained U-Net Model for Few Near Infrared Images","date":"2018-12-22","arxiv_id":"1812.09477","repositories_listed":0,"syntology":null},{"url":null,"slug":"synergy-effect-between-convolutional-neural","title":"Synergy Effect between Convolutional Neural Networks and the Multiplicity of SMILES for Improvement of Molecular Prediction","date":"2018-12-11","arxiv_id":"1812.04439","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-deep-learning-and-argumentative","title":"Combining Deep Learning and Argumentative Reasoning for the Analysis of Social Media Textual Content Using Small Data Sets","date":"2018-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"compact-representation-of-uncertainty-in","title":"Compact Representation of Uncertainty in Clustering","date":"2018-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"modern-neural-networks-generalize-on-small","title":"Modern Neural Networks Generalize on Small Data Sets","date":"2018-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-latent-variable-model-application-to","title":"A Deep Latent-Variable Model Application to Select Treatment Intensity in Survival Analysis","date":"2018-11-29","arxiv_id":"1811.12323","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-tree-based-tensor-formats","title":"Learning with tree-based tensor formats","date":"2018-11-11","arxiv_id":"1811.04455","repositories_listed":0,"syntology":null},{"url":null,"slug":"dragonpaint-rule-based-bootstrapping-for","title":"DragonPaint: Rule based bootstrapping for small data with an application to cartoon coloring","date":"2018-11-07","arxiv_id":"1811.03151","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-shot-learning-via-covariance-preserving","title":"Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks","date":"2018-10-27","arxiv_id":"1810.11730","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-brain-tumor-segmentation-using","title":"Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation","date":"2018-10-18","arxiv_id":"1810.07884","repositories_listed":0,"syntology":null},{"url":null,"slug":"annotation-of-a-large-clinical-entity-corpus","title":"Annotation of a Large Clinical Entity Corpus","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-actively-learn-neural-machine","title":"Learning to Actively Learn Neural Machine Translation","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-gaze-geometry-inferring-novel-3d","title":"Multiple-gaze geometry: Inferring novel 3D locations from gazes observed in monocular video","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"small-sample-learning-in-big-data-era","title":"Small Sample Learning in Big Data Era","date":"2018-08-14","arxiv_id":"1808.04572","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-transferability-of-deep-neural","title":"Improving Transferability of Deep Neural Networks","date":"2018-07-30","arxiv_id":"1807.11459","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-semi-supervised-segmentation-with-weight","title":"Deep semi-supervised segmentation with weight-averaged consistency targets","date":"2018-07-12","arxiv_id":"1807.04657","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-named-entity-recognition-shootout-for","title":"A Named Entity Recognition Shootout for German","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-cnns-rnns-and-weighted-finite-state","title":"Bridging CNNs, RNNs, and Weighted Finite-State Machines","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"network-signatures-from-image-representation","title":"Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification","date":"2018-04-17","arxiv_id":"1804.06275","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-cox-process-inference-using","title":"Large-Scale Cox Process Inference using Variational Fourier Features","date":"2018-04-03","arxiv_id":"1804.01016","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-reinforcement-learning-with-latent","title":"Meta Reinforcement Learning with Latent Variable Gaussian Processes","date":"2018-03-20","arxiv_id":"1803.07551","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-oral-disintegrating-tablet","title":"Predicting Oral Disintegrating Tablet Formulations by Neural Network Techniques","date":"2018-03-14","arxiv_id":"1803.05339","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-contrast-learning","title":"Local Contrast Learning","date":"2018-02-10","arxiv_id":"1802.03499","repositories_listed":0,"syntology":null},{"url":null,"slug":"lie-transform-based-polynomial-neural","title":"Lie Transform--based Neural Networks for Dynamics Simulation and Learning","date":"2018-02-05","arxiv_id":"1802.01353","repositories_listed":0,"syntology":null},{"url":null,"slug":"vibnn-hardware-acceleration-of-bayesian","title":"VIBNN: Hardware Acceleration of Bayesian Neural Networks","date":"2018-02-02","arxiv_id":"1802.00822","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-predictive-approach-using-deep-feature","title":"Representation Learning with Autoencoders for Electronic Health Records: A Comparative Study","date":"2018-01-06","arxiv_id":"1801.02961","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-shot-coresets-the-case-of-k-clustering","title":"One-Shot Coresets: The Case of k-Clustering","date":"2017-11-27","arxiv_id":"1711.09649","repositories_listed":0,"syntology":null},{"url":null,"slug":"ldmnet-low-dimensional-manifold-regularized","title":"LDMNet: Low Dimensional Manifold Regularized Neural Networks","date":"2017-11-16","arxiv_id":"1711.06246","repositories_listed":0,"syntology":null},{"url":null,"slug":"capturing-localized-image-artifacts-through-a","title":"Capturing Localized Image Artifacts through a CNN-based Hyper-image Representation","date":"2017-11-14","arxiv_id":"1711.04945","repositories_listed":0,"syntology":null},{"url":null,"slug":"quality-efficiency-trade-offs-in-machine","title":"Quality-Efficiency Trade-offs in Machine Learning for Text Processing","date":"2017-11-07","arxiv_id":"1711.02295","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-and-reranking-using-multiple-models","title":"Ensemble and Reranking: Using Multiple Models in the NICT-2 Neural Machine Translation System at WAT2017","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/improving-landmark-localization-with-semi","slug":"improving-landmark-localization-with-semi","title":"Improving Landmark Localization with Semi-Supervised Learning","date":"2017-09-05","arxiv_id":"1709.01591","repositories_listed":0,"syntology":null},{"url":null,"slug":"fighting-or-conflict-an-approach-to-revealing","title":"`Fighting' or `Conflict'? An Approach to Revealing Concepts of Terms in Political Discourse","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-on-large-and-small","title":"Representation Learning on Large and Small Data","date":"2017-07-25","arxiv_id":"1707.09873","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-like-humans-with-deep-symbolic","title":"Learning like humans with Deep Symbolic Networks","date":"2017-07-11","arxiv_id":"1707.03377","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simple-efficient-density-estimator-that","title":"A simple efficient density estimator that enables fast systematic search","date":"2017-07-03","arxiv_id":"1707.00783","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthesizing-deep-neural-network","title":"Synthesizing Deep Neural Network Architectures using Biological Synaptic Strength Distributions","date":"2017-07-01","arxiv_id":"1707.00081","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-structure-evolving-lstm","title":"Interpretable Structure-Evolving LSTM","date":"2017-03-08","arxiv_id":"1703.03055","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-elicitation-of-knowledge-on","title":"Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets","date":"2016-12-07","arxiv_id":"1612.02487","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-word-alignment-for-low-resource","title":"Improving word alignment for low resource languages using English monolingual SRL","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"qcri-dsl-2016-spoken-arabic-dialect","title":"QCRI @ DSL 2016: Spoken Arabic Dialect Identification Using Textual Features","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"relaxed-earth-movers-distances-for-chain-and","title":"Relaxed Earth Mover's Distances for Chain- and Tree-connected Spaces and their use as a Loss Function in Deep Learning","date":"2016-11-22","arxiv_id":"1611.07573","repositories_listed":0,"syntology":null},{"url":null,"slug":"nazr-cnn-fine-grained-classification-of-uav","title":"Nazr-CNN: Fine-Grained Classification of UAV Imagery for Damage Assessment","date":"2016-11-20","arxiv_id":"1611.06474","repositories_listed":0,"syntology":null},{"url":null,"slug":"ordinal-constrained-binary-code-learning-for","title":"Ordinal Constrained Binary Code Learning for Nearest Neighbor Search","date":"2016-11-19","arxiv_id":"1611.06362","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-fine-tuning-a-modular-approach-to","title":"Beyond Fine Tuning: A Modular Approach to Learning on Small Data","date":"2016-11-06","arxiv_id":"1611.01714","repositories_listed":0,"syntology":null},{"url":"/paper/facenet2expnet-regularizing-a-deep-face","slug":"facenet2expnet-regularizing-a-deep-face","title":"FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition","date":"2016-09-21","arxiv_id":"1609.06591","repositories_listed":0,"syntology":null},{"url":null,"slug":"facial-expression-recognition-using-a-hybrid","title":"Facial Expression Recognition Using a Hybrid CNN-SIFT Aggregator","date":"2016-08-09","arxiv_id":"1608.02833","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficiently-bounding-optimal-solutions-after","title":"Efficiently Bounding Optimal Solutions after Small Data Modification in Large-Scale Empirical Risk Minimization","date":"2016-06-01","arxiv_id":"1606.00136","repositories_listed":0,"syntology":null},{"url":null,"slug":"frankenstein-learning-deep-face","title":"Frankenstein: Learning Deep Face Representations using Small Data","date":"2016-03-21","arxiv_id":"1603.06470","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-detection-of-non-technical-losses","title":"Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets","date":"2016-02-26","arxiv_id":"1602.08350","repositories_listed":0,"syntology":null},{"url":null,"slug":"maximum-likelihood-estimation-for-single","title":"Maximum Likelihood Estimation for Single Linkage Hierarchical Clustering","date":"2015-11-25","arxiv_id":"1511.07944","repositories_listed":0,"syntology":null},{"url":null,"slug":"l1-logistic-regression-as-a-feature-selection","title":"L1 logistic regression as a feature selection step for training stable classification trees for the prediction of severity criteria in imported malaria","date":"2015-11-20","arxiv_id":"1511.06663","repositories_listed":0,"syntology":null},{"url":"/paper/massive-online-crowdsourced-study-of","slug":"massive-online-crowdsourced-study-of","title":"Massive Online Crowdsourced Study of Subjective and Objective Picture Quality","date":"2015-11-09","arxiv_id":"1511.02919","repositories_listed":0,"syntology":null},{"url":null,"slug":"big-data-small-data-in-domain-out-of-domain","title":"Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representations on Sequence Labelling Tasks","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-improved-deep-learning-architecture-for","title":"An Improved Deep Learning Architecture for Person Re-Identification","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"big-data-small-data-in-domain-out-of-domain-1","title":"Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representation on Sequence Labelling Tasks","date":"2015-04-21","arxiv_id":"1504.05319","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-pca-algorithms-in-distributed","title":"Analysis of PCA Algorithms in Distributed Environments","date":"2015-03-17","arxiv_id":"1503.05214","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-hybrid-cnn-ais-visual-pattern","title":"A Novel Hybrid CNN-AIS Visual Pattern Recognition Engine","date":"2015-03-11","arxiv_id":"1503.03270","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-dimensional-low-rank-plus-sparse-matrix","title":"High Dimensional Low Rank plus Sparse Matrix Decomposition","date":"2015-02-01","arxiv_id":"1502.00182","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-learning-of-ncm-forests-for-large","title":"Incremental Learning of NCM Forests for Large-Scale Image Classification","date":"2014-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"credal-model-averaging-for-classification","title":"Credal Model Averaging for classification: representing prior ignorance and expert opinions","date":"2014-05-14","arxiv_id":"1405.3559","repositories_listed":0,"syntology":null},{"url":null,"slug":"united-statistical-algorithm-small-and-big","title":"United Statistical Algorithm, Small and Big Data: Future OF Statistician","date":"2013-08-02","arxiv_id":"1308.0641","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-biased-are-maximum-entropy-models","title":"How biased are maximum entropy models?","date":"2011-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"f18d30b6519c2eedc6ae2d229c786e1ed42893d6a1586e823606d89990969925","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}