{"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/contrastive-learning/papers/63","list_of":"/task/contrastive-learning","task":"Contrastive Learning","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":63,"pages_in_order":67,"rows_per_page":100,"rows":[6201,6300],"of":6661,"counts":{"archive_papers_tagged":6661,"with_a_code_link":3104,"where_syntology_ran_a_sample":920,"not_listed_spam_title":0,"listed":6661,"listed_where_code_ran":920,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":795,"every_run_a_failure_of_syntologys_instrument":125,"listed_with_a_run_with_no_instrument_failure":795,"listed_every_run_a_failure_of_syntologys_instrument":125,"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/contrastive-learning","prev":"/task/contrastive-learning/papers/62","next":"/task/contrastive-learning/papers/64","papers":[{"url":null,"slug":"rethinking-self-supervision-objectives-for-1","title":"Rethinking Self-Supervision Objectives for Generalizable Coherence Modeling","date":"2021-10-14","arxiv_id":"2110.07198","repositories_listed":0,"syntology":null},{"url":null,"slug":"attentive-and-contrastive-learning-for-joint-1","title":"Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation","date":"2021-10-13","arxiv_id":"2110.06853","repositories_listed":0,"syntology":null},{"url":null,"slug":"false-negative-distillation-and-contrastive","title":"False Negative Distillation and Contrastive Learning for Personalized Outfit Recommendation","date":"2021-10-13","arxiv_id":"2110.06483","repositories_listed":0,"syntology":null},{"url":null,"slug":"inconsistent-few-shot-relation-classification","title":"Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning","date":"2021-10-13","arxiv_id":"2110.08254","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-spatiotemporal-augmentations-on-1","title":"The Impact of Spatiotemporal Augmentations on Self-Supervised Audiovisual Representation Learning","date":"2021-10-13","arxiv_id":"2110.07082","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-representation-learning-for-3d","title":"Unsupervised Contrastive Learning with Simple Transformation for 3D Point Cloud Data","date":"2021-10-13","arxiv_id":"2110.06632","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-learning-through-time-1","title":"Contrastive Learning Through Time","date":"2021-10-12","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"wav2vec-switch-contrastive-learning-from","title":"Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition","date":"2021-10-11","arxiv_id":"2110.04934","repositories_listed":0,"syntology":null},{"url":"/paper/focus-your-distribution-coarse-to-fine-non","slug":"focus-your-distribution-coarse-to-fine-non","title":"Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization","date":"2021-10-09","arxiv_id":"2110.04538","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-learning-for-source-code-with","title":"Towards Learning (Dis)-Similarity of Source Code from Program Contrasts","date":"2021-10-08","arxiv_id":"2110.03868","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-string-representation-learning","title":"Contrastive String Representation Learning using Synthetic Data","date":"2021-10-08","arxiv_id":"2110.04217","repositories_listed":0,"syntology":null},{"url":null,"slug":"scala-supervised-contrastive-learning-for-end","title":"SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition","date":"2021-10-08","arxiv_id":"2110.04187","repositories_listed":0,"syntology":null},{"url":null,"slug":"mc-lcr-multi-modal-contrastive-classification","title":"MC-LCR: Multi-modal contrastive classification by locally correlated representations for effective face forgery detection","date":"2021-10-07","arxiv_id":"2110.03290","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-contrastive-learning-and-pseudolabels","title":"Using Contrastive Learning and Pseudolabels to learn representations for Retail Product Image Classification","date":"2021-10-07","arxiv_id":"2110.03639","repositories_listed":0,"syntology":null},{"url":null,"slug":"activematch-end-to-end-semi-supervised-active","title":"ActiveMatch: End-to-end Semi-supervised Active Representation Learning","date":"2021-10-06","arxiv_id":"2110.02521","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-learning-for-unsupervised-radar","title":"Contrastive Learning for Unsupervised Radar Place Recognition","date":"2021-10-06","arxiv_id":"2110.02744","repositories_listed":0,"syntology":null},{"url":null,"slug":"cut-the-carp-fishing-for-zero-shot-story","title":"Cut the CARP: Fishing for zero-shot story evaluation","date":"2021-10-06","arxiv_id":"2110.03111","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-power-of-contrast-for-feature-learning-a","title":"The Power of Contrast for Feature Learning: A Theoretical Analysis","date":"2021-10-06","arxiv_id":"2110.02473","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-fraud-detection-on-non-attributed-graph","title":"Deep Fraud Detection on Non-attributed Graph","date":"2021-10-04","arxiv_id":"2110.01171","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-out-of-the-box-frameworks-for-unpaired","title":"Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An approach for the crossMoDA Challenge","date":"2021-10-02","arxiv_id":"2110.01607","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-contrastive-learning","title":"Stochastic Contrastive Learning","date":"2021-10-01","arxiv_id":"2110.00552","repositories_listed":0,"syntology":null},{"url":null,"slug":"crossclr-cross-modal-contrastive-learning-for","title":"CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations","date":"2021-09-30","arxiv_id":"2109.14910","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-few-shot-action-recognition-via","title":"Unsupervised Few-Shot Action Recognition via Action-Appearance Aligned Meta-Adaptation","date":"2021-09-30","arxiv_id":"2109.15317","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-rate-distortion-approach-to-domain","title":"A Rate-Distortion Approach to Domain Generalization","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-transferable-general-purpose-predictor-for","title":"A Transferable General-Purpose Predictor for Neural Architecture Search","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"aavae-augmentation-augmented-variational-1","title":"AAVAE: Augmentation-Augmented Variational Autoencoders","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"chaos-is-a-ladder-a-new-understanding-of","title":"Chaos is a Ladder: A New Understanding of Contrastive Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"confess-a-framework-for-single-source-cross","title":"ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"context-invariant-multi-variate-time-series","title":"Context-invariant, multi-variate time series representations","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-learning-is-just-meta-learning","title":"Contrastive Learning is Just Meta-Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-learning-of-3d-shape-descriptor","title":"Contrastive Learning of 3D Shape Descriptor with Dynamic Adversarial Views","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-mutual-information-maximization","title":"Contrastive Mutual Information Maximization for Binary Neural Networks","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-pre-training-for-zero-shot","title":"Contrastive Pre-training for Zero-Shot Information Retrieval","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-quant-quantization-makes-stronger","title":"Contrastive Quant: Quantization Makes Stronger Contrastive Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-video-language-segmentation","title":"Contrastive Video-Language Segmentation","date":"2021-09-29","arxiv_id":"2109.14131","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastively-enforcing-distinctiveness-for","title":"Contrastively Enforcing Distinctiveness for Multi-Label Classification","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"data-efficient-contrastive-learning-by","title":"Data-Efficient Contrastive Learning by Differentiable Hard Sample and Hard Positive Pair Generation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"esco-towards-provably-effective-and-scalable","title":"ESCo: Towards Provably Effective and Scalable Contrastive Representation Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"f-mutual-information-contrastive-learning","title":"$f$-Mutual Information Contrastive Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-contrastive-learning-for-privacy","title":"Federated Contrastive Learning for Privacy-Preserving Unpaired Image-to-Image Translation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-contrastive-representation-learning","title":"Federated Contrastive Representation Learning with Feature Fusion and Neighborhood Matching","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-self-supervised-contrastive","title":"Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning","date":"2021-09-29","arxiv_id":"2109.14611","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-software-vulnerability-detection","title":"Fine-grained Software Vulnerability Detection via Information Theory and Contrastive Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"gental-generative-denoising-skip-gram","title":"GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-cross-contrastive-learning-of","title":"Hierarchical Cross Contrastive Learning of Visual Representations","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"how-does-contrastive-pre-training-connect","title":"How does Contrastive Pre-training Connect Disparate Domains?","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-dynamic-contrast-and-probability","title":"Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id","date":"2021-09-29","arxiv_id":"2109.14157","repositories_listed":0,"syntology":null},{"url":null,"slug":"identity-disentangled-adversarial","title":"Identity-Disentangled Adversarial Augmentation for Self-supervised Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"inductive-biases-for-contrastive-learning-of","title":"Inductive-Biases for Contrastive Learning of Disentangled Representations","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"information-aware-time-series-meta","title":"Information-Aware Time Series Meta-Contrastive Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"interrogating-paradigms-in-self-supervised","title":"Interrogating Paradigms in Self-supervised Graph Representation Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-bilinear-temporal-spectral-fusion","title":"Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Representation Learning in Time Series","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-universal-user-representations-via","title":"Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"m-mix-generating-hard-negatives-via-multiple","title":"$m$-mix: Generating hard negatives via multiple samples mixing for contrastive learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"memrein-rein-the-domain-shift-for-cross","title":"MemREIN: Rein the Domain Shift for Cross-Domain Few-Shot Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-domain-self-supervised-learning","title":"Multi-Domain Self-Supervised Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"not-all-regions-are-worthy-to-be-distilled","title":"Not All Regions are Worthy to be Distilled: Region-aware Knowledge Distillation Towards Efficient Image-to-Image Translation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"prototypical-contrastive-predictive-coding","title":"Prototypical Contrastive Predictive Coding","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-disentanglement-in-generative","title":"Representation Disentanglement in Generative Models with Contrastive Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"residual-contrastive-learning-unsupervised","title":"Residual Contrastive Learning: Unsupervised Representation Learning from Residuals","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-temperature-in-graph-contrastive","title":"Rethinking Temperature in Graph Contrastive Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"s-3-adnet-sequential-anomaly-detection-with","title":"S$^3$ADNet: Sequential Anomaly Detection with Pessimistic Contrastive Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-contrastive-learning-1","title":"Self-Contrastive Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-for-sequential","title":"Self-supervised Learning for Sequential Recommendation with Model Augmentation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"simmer-simple-maximization-of-entropy-and","title":"SimMER: Simple Maximization of Entropy and Rank for Self-supervised Representation Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"synclr-a-synthesis-framework-for-contrastive","title":"SynCLR: A Synthesis Framework for Contrastive Learning of out-of-domain Speech Representations","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-details-matter-preventing-class-collapse","title":"The Details Matter: Preventing Class Collapse in Supervised Contrastive Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-better-understanding-and-better","title":"Towards Better Understanding and Better Generalization of Low-shot Classification in Histology Images with Contrastive Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-self-supervised-learning-via","title":"Understanding Self-supervised Learning via Information Bottleneck Principle","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-contrastive-learning-for-signal","title":"Unsupervised Contrastive Learning for Signal-Dependent Noise Synthesis","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-vision-language-grammar","title":"Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"what-makes-for-good-representations-for","title":"What Makes for Good Representations for Contrastive Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-cl-instance-and-feature-decorrelation","title":"Zero-CL: Instance and Feature decorrelation for negative-free symmetric contrastive learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-contrastive-learning-approach-to-auroral","title":"A Contrastive Learning Approach to Auroral Identification and Classification","date":"2021-09-28","arxiv_id":"2109.13899","repositories_listed":0,"syntology":null},{"url":null,"slug":"modelling-neighbor-relation-in-joint-space","title":"Modelling Neighbor Relation in Joint Space-Time Graph for Video Correspondence Learning","date":"2021-09-28","arxiv_id":"2109.13499","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-jhu-submission-to-voxsrc-21-track-3","title":"The JHU submission to VoxSRC-21: Track 3","date":"2021-09-28","arxiv_id":"2109.13425","repositories_listed":0,"syntology":null},{"url":null,"slug":"click-through-rate-prediction-with-auto","title":"Click-through Rate Prediction with Auto-Quantized Contrastive Learning","date":"2021-09-27","arxiv_id":"2109.13921","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-modeling-of-hand-object-interactions-1","title":"Dynamic Modeling of Hand-Object Interactions via Tactile Sensing.","date":"2021-09-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cluster-analysis-with-deep-embeddings-and","title":"Cluster Analysis with Deep Embeddings and Contrastive Learning","date":"2021-09-26","arxiv_id":"2109.12714","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-video-representation-learning-9","title":"Self-Supervised Video Representation Learning by Video Incoherence Detection","date":"2021-09-26","arxiv_id":"2109.12493","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-learning-for-mitochondria","title":"Contrastive Learning for Mitochondria Segmentation","date":"2021-09-25","arxiv_id":"2109.12363","repositories_listed":0,"syntology":null},{"url":null,"slug":"dense-contrastive-visual-linguistic","title":"Dense Contrastive Visual-Linguistic Pretraining","date":"2021-09-24","arxiv_id":"2109.11778","repositories_listed":0,"syntology":null},{"url":null,"slug":"long-short-view-feature-decomposition-via","title":"Long Short View Feature Decomposition via Contrastive Video Representation Learning","date":"2021-09-23","arxiv_id":"2109.11593","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-for-semi-supervised-1","title":"Self-supervised Learning for Semi-supervised Temporal Language Grounding","date":"2021-09-23","arxiv_id":"2109.11475","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-learning-for-fair-representations","title":"Contrastive Learning for Fair Representations","date":"2021-09-22","arxiv_id":"2109.10645","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-compositional-color","title":"Generating Compositional Color Representations from Text","date":"2021-09-22","arxiv_id":"2109.10477","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-contrastive-representation-for","title":"Learning Contrastive Representation for Semantic Correspondence","date":"2021-09-22","arxiv_id":"2109.10967","repositories_listed":0,"syntology":null},{"url":null,"slug":"homography-augumented-momentum-constrastive","title":"Homography augumented momentum constrastive learning for SAR image retrieval","date":"2021-09-21","arxiv_id":"2109.10329","repositories_listed":0,"syntology":null},{"url":null,"slug":"vpn-video-provenance-network-for-robust","title":"VPN: Video Provenance Network for Robust Content Attribution","date":"2021-09-21","arxiv_id":"2109.10038","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-learning-of-subject-invariant-eeg","title":"Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition","date":"2021-09-20","arxiv_id":"2109.09559","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-training-with-contrastive","title":"Adversarial Training with Contrastive Learning in NLP","date":"2021-09-19","arxiv_id":"2109.09075","repositories_listed":0,"syntology":null},{"url":null,"slug":"interest-oriented-universal-user","title":"Interest-oriented Universal User Representation via Contrastive Learning","date":"2021-09-18","arxiv_id":"2109.08865","repositories_listed":0,"syntology":null},{"url":null,"slug":"intra-inter-subject-self-supervised-learning","title":"Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals","date":"2021-09-18","arxiv_id":"2109.08908","repositories_listed":0,"syntology":null},{"url":null,"slug":"mm-deacon-multimodal-molecular-domain","title":"Multilingual Molecular Representation Learning via Contrastive Pre-training","date":"2021-09-18","arxiv_id":"2109.08830","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-few-shot-intent","title":"Semi-Supervised Few-Shot Intent Classification and Slot Filling","date":"2021-09-17","arxiv_id":"2109.08754","repositories_listed":0,"syntology":null},{"url":null,"slug":"dense-semantic-contrast-for-self-supervised","title":"Dense Semantic Contrast for Self-Supervised Visual Representation Learning","date":"2021-09-16","arxiv_id":"2109.07756","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-bregman-divergence-for-contrastive","title":"Deep Bregman Divergence for Contrastive Learning of Visual Representations","date":"2021-09-15","arxiv_id":"2109.07455","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-contrastive-learning-for","title":"Federated Contrastive Learning for Decentralized Unlabeled Medical Images","date":"2021-09-15","arxiv_id":"2109.07504","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-gradient-based-adversarial-training","title":"Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder","date":"2021-09-14","arxiv_id":"2109.06536","repositories_listed":0,"syntology":null},{"url":null,"slug":"kfcnet-knowledge-filtering-and-contrastive","title":"KFCNet: Knowledge Filtering and Contrastive Learning Network for Generative Commonsense Reasoning","date":"2021-09-14","arxiv_id":"2109.06704","repositories_listed":0,"syntology":null}],"record_sha256":"8bb142e68ff96585500649eefbe3608ccb50ddd70cc2ef8186f9c7abe4644a7b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}