{"id":33158593,"url":"https://github.com/Deepest-Project/meta-learning-study","last_synced_at":"2025-11-20T14:02:33.772Z","repository":{"id":108755069,"uuid":"224847776","full_name":"Deepest-Project/meta-learning-study","owner":"Deepest-Project","description":"Deepest Season 6 Meta-Learning study papers plus alpha","archived":false,"fork":false,"pushed_at":"2020-03-04T05:39:59.000Z","size":34,"stargazers_count":23,"open_issues_count":0,"forks_count":2,"subscribers_count":2,"default_branch":"master","last_synced_at":"2024-06-25T05:35:31.094Z","etag":null,"topics":["few-shot-learning","maml","meta-learning","papers","prototypical-networks","shot-learning"],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Deepest-Project.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2019-11-29T12:12:12.000Z","updated_at":"2022-07-27T01:56:56.000Z","dependencies_parsed_at":"2023-05-14T03:45:10.213Z","dependency_job_id":null,"html_url":"https://github.com/Deepest-Project/meta-learning-study","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Deepest-Project/meta-learning-study","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Deepest-Project%2Fmeta-learning-study","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Deepest-Project%2Fmeta-learning-study/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Deepest-Project%2Fmeta-learning-study/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Deepest-Project%2Fmeta-learning-study/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Deepest-Project","download_url":"https://codeload.github.com/Deepest-Project/meta-learning-study/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Deepest-Project%2Fmeta-learning-study/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":285447937,"owners_count":27173436,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-11-20T02:00:05.334Z","response_time":54,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["few-shot-learning","maml","meta-learning","papers","prototypical-networks","shot-learning"],"created_at":"2025-11-15T21:00:27.010Z","updated_at":"2025-11-20T14:02:33.765Z","avatar_url":"https://github.com/Deepest-Project.png","language":null,"funding_links":[],"categories":["Uncategorized"],"sub_categories":["Uncategorized"],"readme":"# Meta-Learning-Study\nDeepest Season 6 Meta-Learning study papers plus alpha  \n  \nThose who are new to meta-learning, I recommend to start with reading these\n+ Model-agnostic Meta-Learning for Fast Adaptation of Deep Networks\n+ Prototypical Networks for Few-shot Learning\n+ ICML 2019 Meta-Learning Tutorial [[link]](https://sites.google.com/view/icml19metalearning)\n+ CS 330: Deep Multi-Task and Meta Learning [[link]](http://cs330.stanford.edu/)\n\n## Optimization-based Meta-Learning\n+ Model-agnostic Meta-Learning for Fast Adaptation of Deep Networks, (ICML 2017), [[link]](https://arxiv.org/abs/1703.03400)\n+ Meta-Learning with Latent Embedding Optimization, (ICLR 2019), [[link]](https://arxiv.org/abs/1807.05960)\n+ How to Train Your MAML, (ICLR 2019), [[link]](https://arxiv.org/abs/1810.09502)\n+ Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML, (NeurIPs 2019 workshop)[[link]](https://arxiv.org/abs/1909.09157)\n+ Meta-Learning with Implicit Gradients, (NIPS 2019), [[link]](https://arxiv.org/abs/1909.04630)\n+ Meta-Learning with Warped Gradient Descent, (ICLR 2020), [[link]](https://openreview.net/forum?id=rkeiQlBFPB)\n\n## Metric-Learning based Meta-Learning\n+ Prototypical Networks for Few-shot Learning, (NIPS 2017), [[link]](https://arxiv.org/abs/1703.05175)\n+ Learning to Compare: Relation Network for Few-Shot Learning, (CVPR 2018), [[link]](https://arxiv.org/abs/1711.06025)\n+ TADAM: Task dependent adaptive metric for improved few-shot learning, (NIPS 2018)[[link]](https://arxiv.org/abs/1805.10123)\n+ Infinite Mixture Prototypes for Few-Shot Learning, (ICML 2019), [[link]](https://arxiv.org/abs/1902.04552)\n\n## Black-box adaptation based Meta-Learning\n+ One-shot Learning with Memory-Augmented Neural Networks, (ArXiv 2016), [[link]](https://arxiv.org/abs/1605.06065)\n+ Learning to learn by gradient descent by gradient descent, (NIPS 2016), [[link]](https://arxiv.org/abs/1606.04474)\n+ A Simple Neural Attentive Meta-Learner, (ICLR 2018), [[link]](https://arxiv.org/abs/1707.03141)\n+ Meta-Learning with Differentiable Convex Optimization, (CVPR 2019), [[link]](https://arxiv.org/abs/1904.03758)\n\n## Bayesian Approaches\n+ Towards a Neural Statistician, (ICLR 2017), [[link]](https://arxiv.org/abs/1606.02185)\n+ Conditional Neural Processes, (ICML 2018), [[link]](https://arxiv.org/abs/1807.01613)\n+ Probabilistic Model-Agnostic Meta-Learning, (NIPS 2018), [[link]](https://arxiv.org/abs/1806.02817)\n\n## Generation\n+ Few-Shot Adversarial Learning of Realistic Neural Talking Head Models, (ICCV 2019), [[link]](https://arxiv.org/abs/1905.08233)\n+ Few-Shot Adaptive Gaze Estimation, (ICCV 2019), [[link]](https://arxiv.org/abs/1905.01941)\n+ MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targets, (AAAI 2020), [[link]](https://arxiv.org/abs/1911.08139)\n+ MetaPix: Few-Shot Video Retargeting, (ICLR 2020), [[link]](https://openreview.net/forum?id=SJx1URNKwH)\n\n## Unsupervised, Representation\n+ Unsupervised Learning via Meta-Learning, (ICLR 2019), [[link]](https://arxiv.org/abs/1810.02334)\n+ Meta-Learning Update Rules for Unsupervised Representation Learning, (ICLR 2019), [[link]](https://arxiv.org/abs/1804.00222)\n\n## Realistic Setting\n+ A Closer Look at Few-shot Classification, (ICLR 2019), [[link]](https://arxiv.org/abs/1904.04232)\n+ Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples, (ICLR 2020 under review), [[link]](https://arxiv.org/abs/1903.03096)\n+ Meta-Learning without Memorization, (ICLR2020), [[link]](https://arxiv.org/abs/1912.03820)\n\n## Object Detection and Segmentation\n+ CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning, (CVPR 2019), [[link]](https://arxiv.org/abs/1903.02351)\n+ Few-shot Object Detection via Feature Reweighting, (ICCV 2019), [[link]](https://arxiv.org/abs/1812.01866)\n+ Meta-Learning to Detect Rare Objects, (ICCV 2019), [[link]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Meta-Learning_to_Detect_Rare_Objects_ICCV_2019_paper.pdf)\n\n## Self-Supervised Learning\n+ Boosting Few-Shot Visual Learning with Self-Supervision, (ICCV 2019), [[link]](https://arxiv.org/abs/1906.05186)\n+ Charting the Right Manifold: Manifold Mixup for Few-shot Learning, (ArXiv 2019), [[link]](https://arxiv.org/abs/1907.12087)\n\n## Before 2016\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FDeepest-Project%2Fmeta-learning-study","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FDeepest-Project%2Fmeta-learning-study","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FDeepest-Project%2Fmeta-learning-study/lists"}