{"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/few-shot-learning/papers/26","list_of":"/task/few-shot-learning","task":"Few-Shot 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":26,"pages_in_order":30,"rows_per_page":100,"rows":[2501,2600],"of":2964,"counts":{"archive_papers_tagged":2964,"with_a_code_link":1297,"where_syntology_ran_a_sample":373,"not_listed_spam_title":0,"listed":2964,"listed_where_code_ran":373,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":306,"every_run_a_failure_of_syntologys_instrument":67,"listed_with_a_run_with_no_instrument_failure":306,"listed_every_run_a_failure_of_syntologys_instrument":67,"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/few-shot-learning","prev":"/task/few-shot-learning/papers/25","next":"/task/few-shot-learning/papers/27","papers":[{"url":null,"slug":"semi-supervised-few-shot-classification-with","title":"Semi-Supervised Few-Shot Classification with Deep Invertible Hybrid Models","date":"2021-05-22","arxiv_id":"2105.10644","repositories_listed":0,"syntology":null},{"url":null,"slug":"aligning-visual-prototypes-with-bert","title":"Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning","date":"2021-05-21","arxiv_id":"2105.10195","repositories_listed":0,"syntology":null},{"url":"/paper/compositional-fine-grained-low-shot-learning","slug":"compositional-fine-grained-low-shot-learning","title":"Compositional Fine-Grained Low-Shot Learning","date":"2021-05-21","arxiv_id":"2105.10438","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-knowledge-enhanced-bayesian-meta","title":"Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection","date":"2021-05-20","arxiv_id":"2105.09509","repositories_listed":0,"syntology":null},{"url":null,"slug":"hetmaml-task-heterogeneous-model-agnostic","title":"HetMAML: Task-Heterogeneous Model-Agnostic Meta-Learning for Few-Shot Learning Across Modalities","date":"2021-05-17","arxiv_id":"2105.07889","repositories_listed":0,"syntology":null},{"url":null,"slug":"livewired-neural-networks-making-neurons-that","title":"Livewired Neural Networks: Making Neurons That Fire Together Wire Together","date":"2021-05-17","arxiv_id":"2105.08111","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-contrastive-learning-with","title":"Semi-supervised Contrastive Learning with Similarity Co-calibration","date":"2021-05-16","arxiv_id":"2105.07387","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-making-few-shot-learning-stronger","title":"Ensemble Making Few-Shot Learning Stronger","date":"2021-05-12","arxiv_id":"2105.11904","repositories_listed":0,"syntology":null},{"url":"/paper/distribution-matching-for-heterogeneous-multi","slug":"distribution-matching-for-heterogeneous-multi","title":"Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study","date":"2021-05-08","arxiv_id":"2105.03790","repositories_listed":0,"syntology":null},{"url":null,"slug":"pemnet-a-transfer-learning-based-modeling","title":"PEMNET: A Transfer Learning-based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems","date":"2021-05-07","arxiv_id":"2105.03057","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-fine-tuning-allows-for-effective-meta","title":"How Fine-Tuning Allows for Effective Meta-Learning","date":"2021-05-05","arxiv_id":"2105.02221","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-descriptor-based-multi-prototype","title":"Local descriptor-based multi-prototype network for few-shot Learning","date":"2021-05-05","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mcgnet-partial-multi-view-few-shot-learning","title":"Few-shot Partial Multi-view Learning","date":"2021-05-05","arxiv_id":"2105.02046","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-model-to-rule-them-all-towards-zero-shot","title":"One Model to Rule them All: Towards Zero-Shot Learning for Databases","date":"2021-05-03","arxiv_id":"2105.00642","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-framework-for-lifelong","title":"A Deep Learning Framework for Lifelong Machine Learning","date":"2021-05-01","arxiv_id":"2105.00157","repositories_listed":0,"syntology":null},{"url":"/paper/rich-semantics-improve-few-shot-learning","slug":"rich-semantics-improve-few-shot-learning","title":"Rich Semantics Improve Few-shot Learning","date":"2021-04-26","arxiv_id":"2104.12709","repositories_listed":0,"syntology":null},{"url":null,"slug":"demystification-of-few-shot-and-one-shot","title":"Demystification of Few-shot and One-shot Learning","date":"2021-04-25","arxiv_id":"2104.12174","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-continual-learning-a-brain-inspired","title":"Few-shot Continual Learning: a Brain-inspired Approach","date":"2021-04-19","arxiv_id":"2104.09034","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-learning-for-topic-modeling","title":"Few-shot Learning for Topic Modeling","date":"2021-04-19","arxiv_id":"2104.09011","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-wifi-based-activity","title":"Self-Supervised WiFi-Based Activity Recognition","date":"2021-04-19","arxiv_id":"2104.09072","repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-and-cross-lingual-intent","title":"Multilingual and Cross-Lingual Intent Detection from Spoken Data","date":"2021-04-17","arxiv_id":"2104.08524","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-few-shot-relation-classification","title":"Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes","date":"2021-04-17","arxiv_id":"2104.08481","repositories_listed":0,"syntology":null},{"url":null,"slug":"pareto-self-supervised-training-for-few-shot","title":"Pareto Self-Supervised Training for Few-Shot Learning","date":"2021-04-16","arxiv_id":"2104.07841","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedding-adaptation-is-still-needed-for-few","title":"Embedding Adaptation is Still Needed for Few-Shot Learning","date":"2021-04-15","arxiv_id":"2104.07255","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-hypernetworks-for-novel-feature-1","title":"Contextual HyperNetworks for Novel Feature Adaptation","date":"2021-04-12","arxiv_id":"2104.05860","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-intent-classification-and-slot","title":"Few-shot Intent Classification and Slot Filling with Retrieved Examples","date":"2021-04-12","arxiv_id":"2104.05763","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforced-attention-for-few-shot-learning","title":"Reinforced Attention for Few-Shot Learning and Beyond","date":"2021-04-09","arxiv_id":"2104.04192","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-action-recognition-with-compromised","title":"Few-Shot Action Recognition with Compromised Metric via Optimal Transport","date":"2021-04-08","arxiv_id":"2104.03737","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-for-personalized","title":"Efficient Personalized Speech Enhancement through Self-Supervised Learning","date":"2021-04-05","arxiv_id":"2104.02017","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-few-shot-learning-with-adversarial","title":"Federated Few-Shot Learning with Adversarial Learning","date":"2021-04-01","arxiv_id":"2104.00365","repositories_listed":0,"syntology":null},{"url":null,"slug":"story-centaur-large-language-model-few-shot","title":"Story Centaur: Large Language Model Few Shot Learning as a Creative Writing Tool","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"head2headfs-video-based-head-reenactment-with","title":"Head2HeadFS: Video-based Head Reenactment with Few-shot Learning","date":"2021-03-30","arxiv_id":"2103.16229","repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-representation-by-mutual","title":"Explaining Representation by Mutual Information","date":"2021-03-28","arxiv_id":"2103.15114","repositories_listed":0,"syntology":null},{"url":null,"slug":"iup-an-intelligent-utility-prediction-scheme","title":"IUP: An Intelligent Utility Prediction Scheme for Solid-State Fermentation in 5G IoT","date":"2021-03-28","arxiv_id":"2103.15073","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-learning-for-video-object-detection","title":"When Few-Shot Learning Meets Video Object Detection","date":"2021-03-26","arxiv_id":"2103.14724","repositories_listed":0,"syntology":null},{"url":null,"slug":"spirit-distillation-precise-real-time","title":"Spirit Distillation: Precise Real-time Semantic Segmentation of Road Scenes with Insufficient Data","date":"2021-03-25","arxiv_id":"2103.13733","repositories_listed":0,"syntology":null},{"url":"/paper/multi-level-metric-learning-for-few-shot","slug":"multi-level-metric-learning-for-few-shot","title":"Multi-level Metric Learning for Few-shot Image Recognition","date":"2021-03-21","arxiv_id":"2103.11383","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-visual-object-tracking-via-few-shot","title":"Real-Time Visual Object Tracking via Few-Shot Learning","date":"2021-03-18","arxiv_id":"2103.10130","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-method-for-features-normalization-in","title":"A New Method for Features Normalization in Motor Imagery Few-Shot Learning using Resting-State","date":"2021-03-17","arxiv_id":"2103.09507","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-few-shot-fact-checking-via-perplexity","title":"Towards Few-Shot Fact-Checking via Perplexity","date":"2021-03-17","arxiv_id":"2103.09535","repositories_listed":0,"syntology":null},{"url":null,"slug":"dmn4-few-shot-learning-via-discriminative","title":"DMN4: Few-shot Learning via Discriminative Mutual Nearest Neighbor Neural Network","date":"2021-03-15","arxiv_id":"2103.08160","repositories_listed":0,"syntology":null},{"url":null,"slug":"siamese-network-features-for-endoscopy-image","title":"Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video","date":"2021-03-15","arxiv_id":"2103.08504","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-few-shot-learning-approach-for-accelerated","title":"A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priors","date":"2021-03-13","arxiv_id":"2103.07790","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-learning-of-an-interleaved-text","title":"Few-Shot Learning of an Interleaved Text Summarization Model by Pretraining with Synthetic Data","date":"2021-03-08","arxiv_id":"2103.05131","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-learning-for-slot-tagging-with","title":"Few-shot Learning for Slot Tagging with Attentive Relational Network","date":"2021-03-03","arxiv_id":"2103.02333","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hint-from-arithmetic-on-systematic","title":"A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics","date":"2021-03-02","arxiv_id":"2103.01403","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-one-class-classifiers-with","title":"Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection","date":"2021-03-01","arxiv_id":"2103.00684","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-for-learning-generator-from","title":"Domain Adaptation for Learning Generator from Paired Few-Shot Data","date":"2021-02-25","arxiv_id":"2102.12765","repositories_listed":0,"syntology":null},{"url":null,"slug":"enabling-the-network-to-surf-the-internet","title":"Enabling the Network to Surf the Internet","date":"2021-02-24","arxiv_id":"2102.12205","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-learning-for-information","title":"Few Shot Learning for Information Verification","date":"2021-02-22","arxiv_id":"2102.10956","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-learning-for-few-shot-audio","title":"Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining","date":"2021-02-16","arxiv_id":"2102.08074","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-shot-learning-for-the-long-term","title":"One-shot learning for the long term: consolidation with an artificial hippocampal algorithm","date":"2021-02-15","arxiv_id":"2102.07503","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-meta-learning-with-continual","title":"Large-Scale Meta-Learning with Continual Trajectory Shifting","date":"2021-02-14","arxiv_id":"2102.07215","repositories_listed":0,"syntology":null},{"url":"/paper/model-agnostic-graph-regularization-for-few","slug":"model-agnostic-graph-regularization-for-few","title":"Model-Agnostic Graph Regularization for Few-Shot Learning","date":"2021-02-14","arxiv_id":"2102.07077","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-objective-meta-learning","title":"Multi-Objective Meta Learning","date":"2021-02-14","arxiv_id":"2102.07121","repositories_listed":0,"syntology":null},{"url":null,"slug":"partial-is-better-than-all-revisiting-fine","title":"Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning","date":"2021-02-08","arxiv_id":"2102.03983","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-time-series-segmentation-using","title":"Few-shot time series segmentation using prototype-defined infinite hidden Markov models","date":"2021-02-07","arxiv_id":"2102.03885","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperspherical-embedding-for-novel-class","title":"Hyperspherical embedding for novel class classification","date":"2021-02-05","arxiv_id":"2102.03243","repositories_listed":0,"syntology":null},{"url":null,"slug":"metric-embedding-sub-discrimination-study","title":"Feature Representation in Deep Metric Embeddings","date":"2021-02-05","arxiv_id":"2102.03176","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-image-classification-with-multi","title":"Few-shot Image Classification with Multi-Facet Prototypes","date":"2021-02-01","arxiv_id":"2102.00801","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-learning-for-ct-scan-based-covid-19","title":"Few-shot Learning for CT Scan based COVID-19 Diagnosis","date":"2021-02-01","arxiv_id":"2102.00596","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-learning-for-road-object-detection","title":"Few-Shot Learning for Road Object Detection","date":"2021-01-29","arxiv_id":"2101.12543","repositories_listed":0,"syntology":null},{"url":null,"slug":"combat-data-shift-in-few-shot-learning-with","title":"Combat Data Shift in Few-shot Learning with Knowledge Graph","date":"2021-01-27","arxiv_id":"2101.11354","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-contrastive-learning-for-few-shot","title":"Supervised Momentum Contrastive Learning for Few-Shot Classification","date":"2021-01-26","arxiv_id":"2101.11058","repositories_listed":0,"syntology":null},{"url":null,"slug":"atrm-attention-based-task-level-relation","title":"TLRM: Task-level Relation Module for GNN-based Few-Shot Learning","date":"2021-01-25","arxiv_id":"2101.09840","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-prototype-learning-with-augmented","title":"Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning","date":"2021-01-23","arxiv_id":"2101.09499","repositories_listed":0,"syntology":null},{"url":null,"slug":"learn-from-concepts-towards-the-purified","title":"Learn from Concepts: Towards the Purified Memory for Few-shot Learning","date":"2021-01-23","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"stress-testing-of-meta-learning-approaches","title":"Stress Testing of Meta-learning Approaches for Few-shot Learning","date":"2021-01-21","arxiv_id":"2101.08587","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-domain-few-shot-learning-with","title":"Cross-domain few-shot learning with unlabelled data","date":"2021-01-19","arxiv_id":"2101.07899","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-bayesian-optimization-with-deep-1","title":"Few-Shot Bayesian Optimization with Deep Kernel Surrogates","date":"2021-01-19","arxiv_id":"2101.07667","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-with-limited-data","title":"Machine learning with limited data","date":"2021-01-18","arxiv_id":"2101.11461","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-focus-cascaded-feature-matching","title":"Learning to Focus: Cascaded Feature Matching Network for Few-shot Image Recognition","date":"2021-01-13","arxiv_id":"2101.05018","repositories_listed":0,"syntology":null},{"url":null,"slug":"lesion2vec-deep-metric-learning-for-few-shot","title":"Lesion2Vec: Deep Metric Learning for Few-Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy Video","date":"2021-01-11","arxiv_id":"2101.04240","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-propagation-for-few-shot-learning","title":"Local Propagation for Few-Shot Learning","date":"2021-01-05","arxiv_id":"2101.01480","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-lazy-approach-to-long-horizon-gradient","title":"A Lazy Approach to Long-Horizon Gradient-Based Meta-Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-theory-of-self-supervised-framework-for-few","title":"A Theory of Self-Supervised Framework for Few-Shot Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"attacking-few-shot-classifiers-with","title":"Attacking Few-Shot Classifiers with Adversarial Support Sets","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"auto-view-contrastive-learning-for-few-shot","title":"Auto-view contrastive learning for few-shot image recognition","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"class-imbalance-in-few-shot-learning","title":"Class Imbalance in Few-Shot Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"context-agnostic-learning-using-synthetic","title":"Context-Agnostic Learning Using Synthetic Data","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modal-knowledge-enhancement-mechanism","title":"cross-modal knowledge enhancement mechanism for few-shot learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"curvature-generation-in-curved-spaces-for-few","title":"Curvature Generation in Curved Spaces for Few-Shot Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cut-out-the-annotator-keep-the-cutout-better","title":"Cut out the annotator, keep the cutout: better segmentation with weak supervision","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-representation-learning-for","title":"Exploring representation learning for flexible few-shot tasks","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"few-round-learning-for-federated-learning","title":"Few-Round Learning for Federated Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fewmatch-dynamic-prototype-refinement-for","title":"Fewmatch: Dynamic Prototype Refinement for Semi-Supervised Few-Shot Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improve-novel-class-generalization-by","title":"Improve Novel Class Generalization By Adaptive Feature Distribution for Few-Shot Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/incremental-few-shot-learning-via-vector","slug":"incremental-few-shot-learning-via-vector","title":"Incremental few-shot learning via vector quantization in deep embedded space","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-methods-in-hyperbolic-spaces","title":"Kernel Methods in Hyperbolic Spaces","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-semantic-similarities-for","title":"Learning Semantic Similarities for Prototypical Classifiers","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-hallucinate-examples-from","title":"Learning To Hallucinate Examples From Extrinsic and Intrinsic Supervision","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-learn-with-smooth-regularization","title":"Learning to Learn with Smooth Regularization","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"masp-model-agnostic-sample-propagation-for","title":"MASP: Model-Agnostic Sample Propagation for Few-shot learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"melr-meta-learning-via-modeling-episode-level","title":"MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-attack-class-agnostic-and-model-agnostic","title":"Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learned-confidence-for-transductive-few","title":"Meta-Learned Confidence for Transductive Few-shot Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"metanorm-learning-to-normalize-few-shot","title":"MetaNorm: Learning to Normalize Few-Shot Batches Across Domains","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"misclassification-detection-via-class","title":"Misclassification Detection via Class Augmentation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-representation-ensemble-in-few-shot","title":"Multi-Representation Ensemble in Few-Shot Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-role-of-pre-training-for-meta-few-shot","title":"On the Role of Pre-training for Meta Few-Shot Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"6f6db47bc13ee97e971803c5fbd7981be30579127634139c393a4ec455d6d55c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}