{"list":"other","total":51,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"1807.03748","title":"Representation Learning with Contrastive Predictive Coding","created":"2018-07-10","cited_by_count":7777},{"id":"1911.05722","title":"Momentum Contrast for Unsupervised Visual Representation Learning","created":"2019-11-13","cited_by_count":7513},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"1505.07818","title":"Domain-Adversarial Training of Neural Networks","created":"2015-05-28","cited_by_count":3044},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"id":"2004.11362","title":"Supervised Contrastive Learning","created":"2020-04-23","cited_by_count":2829},{"id":"1606.04080","title":"Matching Networks for One Shot Learning","created":"2016-06-13","cited_by_count":2255},{"id":"1805.01978","title":"Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination","created":"2018-05-05","cited_by_count":1845},{"id":"1808.06670","title":"Learning deep representations by mutual information estimation and maximization","created":"2018-08-20","cited_by_count":1273},{"id":"1411.1792","title":"How transferable are features in deep neural networks?","created":"2014-11-06","cited_by_count":1225},{"id":"1711.06025","title":"Learning to Compare: Relation Network for Few-Shot Learning","created":"2017-11-16","cited_by_count":1143},{"id":"1805.09501","title":"AutoAugment: Learning Augmentation Policies from Data","created":"2018-05-24","cited_by_count":730},{"id":"1904.04232","title":"A Closer Look at Few-shot Classification","created":"2019-04-08","cited_by_count":684},{"id":"1905.09272","title":"Data-Efficient Image Recognition with Contrastive Predictive Coding","created":"2019-05-22","cited_by_count":673},{"id":"2010.04592","title":"Contrastive Learning with Hard Negative Samples","created":"2020-10-09","cited_by_count":628},{"id":"1805.08974","title":"Do Better ImageNet Models Transfer Better?","created":"2018-05-23","cited_by_count":565},{"id":"1902.09229","title":"A Theoretical Analysis of Contrastive Unsupervised Representation Learning","created":"2019-02-25","cited_by_count":530},{"id":"1807.05960","title":"Meta-Learning with Latent Embedding Optimization","created":"2018-07-16","cited_by_count":476},{"id":"1803.00676","title":"Meta-Learning for Semi-Supervised Few-Shot Classification","created":"2018-03-02","cited_by_count":456},{"id":"2003.11539","title":"Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?","created":"2020-03-25","cited_by_count":437},{"id":"1804.09458","title":"Dynamic Few-Shot Visual Learning without Forgetting","created":"2018-04-25","cited_by_count":385},{"id":"1909.09157","title":"Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML","created":"2019-09-19","cited_by_count":359},{"id":"1711.04043","title":"Few-Shot Learning with Graph Neural Networks","created":"2017-11-10","cited_by_count":324},{"id":"1903.03096","title":"Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples","created":"2019-03-07","cited_by_count":315},{"id":"1909.02729","title":"A Baseline for Few-Shot Image Classification","created":"2019-09-06","cited_by_count":298},{"id":"1703.00837","title":"Meta Networks","created":"2017-03-02","cited_by_count":269},{"id":"1808.06048","title":"Distractor-aware Siamese Networks for Visual Object Tracking","created":"2018-08-18","cited_by_count":187},{"id":"2001.08735","title":"Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation","created":"2020-01-23","cited_by_count":185},{"id":"1801.05401","title":"Low-Shot Learning from Imaginary Data","created":"2018-01-16","cited_by_count":181},{"id":"2007.11498","title":"CrossTransformers: spatially-aware few-shot transfer","created":"2020-07-22","cited_by_count":174},{"id":"1811.08737","title":"SpotTune: Transfer Learning through Adaptive Fine-tuning","created":"2018-11-21","cited_by_count":168},{"id":"1906.05186","title":"Boosting Few-Shot Visual Learning with Self-Supervision","created":"2019-06-12","cited_by_count":140},{"id":"1906.00562","title":"Learning to Self-Train for Semi-Supervised Few-Shot Classification","created":"2019-06-03","cited_by_count":83},{"id":"1810.02334","title":"Unsupervised Learning via Meta-Learning","created":"2018-10-04","cited_by_count":82},{"id":"1910.03560","title":"When Does Self-supervision Improve Few-shot Learning?","created":"2019-10-08","cited_by_count":78},{"id":"2010.07734","title":"Self-training for Few-shot Transfer Across Extreme Task Differences","created":"2020-10-15","cited_by_count":76},{"id":"1811.11819","title":"Unsupervised Meta-Learning For Few-Shot Image Classification","created":"2018-11-28","cited_by_count":75},{"id":"1903.11341","title":"Diversity with Cooperation: Ensemble Methods for Few-Shot Classification","created":"2019-03-27","cited_by_count":70},{"id":"1912.05094","title":"Associative Alignment for Few-shot Image Classification","created":"2019-12-11","cited_by_count":58},{"id":"1808.04699","title":"Improving Generalization via Scalable Neighborhood Component Analysis","created":"2018-08-14","cited_by_count":53},{"id":"1702.08690","title":"Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning","created":"2017-02-28","cited_by_count":48},{"id":"2012.03515","title":"Fine-grained Angular Contrastive Learning with Coarse Labels","created":"2020-12-07","cited_by_count":30},{"id":"2006.10236","title":"Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models","created":"2020-06-18","cited_by_count":26},{"id":"2001.07926","title":"Optimized Generic Feature Learning for Few-shot Classification across Domains","created":"2020-01-22","cited_by_count":23},{"id":"2012.13831","title":"Spatial Contrastive Learning for Few-Shot Classification","created":"2020-12-26","cited_by_count":17},{"id":"1806.02679","title":"Semi-Supervised Learning via Compact Latent Space Clustering","created":"2018-06-07","cited_by_count":15},{"id":"2006.07540","title":"MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures","created":"2020-06-13","cited_by_count":7}],"next_cursor":null}