{"list":"other","total":58,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"1911.05722","title":"Momentum Contrast for Unsupervised Visual Representation Learning","created":"2019-11-13","cited_by_count":7513},{"id":"1905.11946","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","created":"2019-05-28","cited_by_count":6442},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"id":"1903.12261","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","created":"2019-03-28","cited_by_count":2877},{"id":"2006.09882","title":"Unsupervised Learning of Visual Features by Contrasting Cluster Assignments","created":"2020-06-17","cited_by_count":2857},{"id":"1503.03832","title":"FaceNet: A Unified Embedding for Face Recognition and Clustering","created":"2015-03-12","cited_by_count":2849},{"id":"1902.10811","title":"Do ImageNet Classifiers Generalize to ImageNet?","created":"2019-02-13","cited_by_count":1515},{"id":"2012.07421","title":"WILDS: A Benchmark of in-the-Wild Distribution Shifts","created":"2020-12-14","cited_by_count":1375},{"id":"1912.01991","title":"Self-Supervised Learning of Pretext-Invariant Representations","created":"2019-12-04","cited_by_count":808},{"id":"1703.07737","title":"In Defense of the Triplet Loss for Person Re-Identification","created":"2017-03-22","cited_by_count":786},{"id":"2106.08265","title":"Towards Total Recall in Industrial Anomaly Detection","created":"2021-06-15","cited_by_count":776},{"id":"1912.11370","title":"Big Transfer (BiT): General Visual Representation Learning","created":"2019-12-24","cited_by_count":769},{"id":"1910.10699","title":"Contrastive Representation Distillation","created":"2019-10-23","cited_by_count":748},{"id":"2003.00688","title":"Out-of-Distribution Generalization via Risk Extrapolation (REx)","created":"2020-03-02","cited_by_count":742},{"id":"1704.08063","title":"SphereFace: Deep Hypersphere Embedding for Face Recognition","created":"2017-04-26","cited_by_count":737},{"id":"2104.10972","title":"ImageNet-21K Pretraining for the Masses","created":"2021-04-22","cited_by_count":613},{"id":"1904.03758","title":"Meta-Learning with Differentiable Convex Optimization","created":"2019-04-07","cited_by_count":461},{"id":"1511.06452","title":"Deep Metric Learning via Lifted Structured Feature Embedding","created":"2015-11-19","cited_by_count":449},{"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":"1904.06627","title":"Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning","created":"2019-04-14","cited_by_count":368},{"id":"1909.09157","title":"Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML","created":"2019-09-19","cited_by_count":359},{"id":"2003.08505","title":"A Metric Learning Reality Check","created":"2020-03-18","cited_by_count":321},{"id":"1706.07567","title":"Sampling Matters in Deep Embedding Learning","created":"2017-06-23","cited_by_count":285},{"id":"1703.07464","title":"No Fuss Distance Metric Learning using Proxies","created":"2017-03-21","cited_by_count":226},{"id":"2003.13911","title":"Proxy Anchor Loss for Deep Metric Learning","created":"2020-03-31","cited_by_count":195},{"id":"1704.01719","title":"Beyond triplet loss: a deep quadruplet network for person re-identification","created":"2017-04-06","cited_by_count":172},{"id":"1909.05235","title":"SoftTriple Loss: Deep Metric Learning Without Triplet Sampling","created":"2019-09-11","cited_by_count":163},{"id":"1810.06951","title":"Deep Metric Learning with Hierarchical Triplet Loss","created":"2018-10-16","cited_by_count":112},{"id":"1708.01682","title":"Deep Metric Learning with Angular Loss","created":"2017-08-04","cited_by_count":107},{"id":"2004.01113","title":"ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis","created":"2020-04-02","cited_by_count":107},{"id":"2002.08473","title":"Revisiting Training Strategies and Generalization Performance in Deep Metric Learning","created":"2020-02-19","cited_by_count":100},{"id":"1704.01285","title":"Smart Mining for Deep Metric Learning","created":"2017-04-05","cited_by_count":96},{"id":"2003.01455","title":"Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications","created":"2020-03-03","cited_by_count":80},{"id":"1904.04370","title":"Improved Embeddings with Easy Positive Triplet Mining","created":"2019-04-08","cited_by_count":67},{"id":"1804.00382","title":"Attention-based Ensemble for Deep Metric Learning","created":"2018-04-02","cited_by_count":56},{"id":"1903.05503","title":"Hardness-Aware Deep Metric Learning","created":"2019-03-13","cited_by_count":55},{"id":"1707.02610","title":"Few-Shot Learning Through an Information Retrieval Lens","created":"2017-07-09","cited_by_count":51},{"id":"1909.11574","title":"MIC: Mining Interclass Characteristics for Improved Metric Learning","created":"2019-09-25","cited_by_count":41},{"id":"1801.04815","title":"Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly","created":"2018-01-15","cited_by_count":40},{"id":"2004.13458","title":"DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning","created":"2020-04-28","cited_by_count":40},{"id":"1906.05990","title":"Divide and Conquer the Embedding Space for Metric Learning","created":"2019-06-14","cited_by_count":39},{"id":"2102.07753","title":"Learning Intra-Batch Connections for Deep Metric Learning","created":"2021-02-15","cited_by_count":34},{"id":"1908.02735","title":"Metric Learning With HORDE: High-Order Regularizer for Deep Embeddings","created":"2019-08-07","cited_by_count":29},{"id":"2003.11113","title":"PADS: Policy-Adapted Sampling for Visual Similarity Learning","created":"2020-03-24","cited_by_count":20},{"id":"2004.05582","title":"Sharing Matters for Generalization in Deep Metric Learning","created":"2020-04-12","cited_by_count":15},{"id":"2009.08348","title":"S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning","created":"2020-09-17","cited_by_count":14},{"id":"1911.12528","title":"Unbiased Evaluation of Deep Metric Learning Algorithms","created":"2019-11-28","cited_by_count":10},{"id":"1708.01191","title":"Unsupervised Video Understanding by Reconciliation of Posture Similarities","created":"2017-08-03","cited_by_count":4},{"id":"1911.07808","title":"Unsupervised Representation Learning by Discovering Reliable Image Relations","created":"2019-11-18","cited_by_count":4}],"next_cursor":null}