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Deep Metric Learning (DML) is arguably one of the most influential lines of research for learning visual similarities with many proposed approaches every year.
Least squares quantization in pcm
Lloyd, S. P · 1982
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A simple weight decay can improve generalization
Krogh, A. and Hertz, J. A · 1992
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The earth mover’s distance as a metric for image retrieval
Rubner, Y., Tomasi, C., and Guibas, L. J · 2000
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Geometric approximation via coresets
Agarwal, P. K., Har-Peled, S., and Varadarajan, K. R · 2005
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Dimensionality reduction by learning an invariant mapping
Hadsell, R., Chopra, S., and LeCun, Y · 2006
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ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Introduction to information retrieval
Manning, C., Raghavan, P., and Schütze, H · 2010
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Product quantization for nearest neighbor search
Jegou, H., Douze, M., and Schmid, C · 2011
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The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
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Supervised metric learning with generalization guarantees, 2013
Bellet, A · 2013
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3d object representations for fine-grained categorization
Krause, J., Stark, M., Deng, J., and Fei-Fei, L · 2013
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Robustness and generalization for metric learning
Bellet, A. and Habrard, A · 2014
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Discriminative deep metric learning for face verification in the wild
Hu, J., Lu, J., and Tan, Y · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Facenet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., and Philbin, J · 2015
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
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Deep learning and the information bottleneck principle, 2015
Tishby, N. and Zaslavsky, N · 2015
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Information dropout: Learning optimal representations through noisy computation, 2016
Achille, A. and Soatto, S · 2016
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Deep variational information bottleneck, 2016
Alemi, A. A., Fischer, I., Dillon, J. V., and Murphy, K · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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On large-batch training for deep learning: Generalization gap and sharp minima
Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M., and Tang, P. T. P · 2016
Earlier work this paper cites.
Deep metric learning via lifted structured feature embedding
Oh Song, H., Xiang, Y., Jegelka, S., and Savarese, S · 2016
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Improved deep metric learning with multi-class n-pair loss objective
Sohn, K · 2016
Cited alongside, same era.
Learning deep embeddings with histogram loss
Ustinova, E. and Lempitsky, V · 2016
Cited alongside, same era.
Beyond triplet loss: a deep quadruplet network for person re-identification
Chen, W., Chen, X., Zhang, J., and Huang, K · 2017
Cited alongside, same era.
Accurate, large minibatch sgd: Training imagenet in 1 hour
Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K · 2017
Cited alongside, same era.
Smart mining for deep metric learning
Harwood, B., Kumar, B., Carneiro, G., Reid, I., Drummond, T., et al · 2017
Cited alongside, same era.
In defense of the triplet loss for person re-identification, 2017
Deep metric learning with bier: Boosting independent embeddings robustly
Opitz, M., Waltner, G., Possegger, H., and Bischof, H · 2018
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Manifold mixup: Better representations by interpolating hidden states, 2018
Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., and Bengio, Y · 2018
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Deep randomized ensembles for metric learning
Xuan, H., Souvenir, R., and Pless, R · 2018
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Correcting the triplet selection bias for triplet loss
Yu, B., Liu, T., Gong, M., Ding, C., and Tao, D · 2018
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Classification is a strong baseline for deep metric learning, 2018
Zhai, A. and Wu, H.-Y · 2018
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Unreproducible research is reproducible
Bouthillier, X., Laurent, C., and Vincent, P · 2019
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Hermans, A., Beyer, L., and Leibe, B · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium, 2017
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Cited alongside, same era.
Sphereface: Deep hypersphere embedding for face recognition
Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., and Song, L · 2017
Cited alongside, same era.
No fuss distance metric learning using proxies
Movshovitz-Attias, Y., Toshev, A., Leung, T. K., Ioffe, S., and Singh, S · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
Cited alongside, same era.
Opening the black box of deep neural networks via information, 2017
Shwartz-Ziv, R. and Tishby, N · 2017
Cited alongside, same era.
Don’t decay the learning rate, increase the batch size
Smith, S. L., Kindermans, P.-J., Ying, C., and Le, Q. V · 2017
Cited alongside, same era.
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Deep metric learning to rank
Cakir, F., He, K., Xia, X., Kulis, B., and Sclaroff, S · 2019
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Infobot: Transfer and exploration via the information bottleneck, 2019
Goyal, A., Islam, R., Strouse, D., Ahmed, Z., Botvinick, M., Larochelle, H., Bengio, Y., and Levine, S · 2019
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Deep metric learning: The generalization analysis and an adaptive algorithm
Huai, M., Xue, H., Miao, C., Yao, L., Su, L., Chen, C., and Zhang, A · 2019
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Metric learning with horde: High-order regularizer for deep embeddings
Jacob, P., Picard, D., Histace, A., and Klein, E · 2019
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Self-supervised learning of pretext-invariant representations, 2019
Misra, I. and van der Maaten, L · 2019
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Softtriple loss: Deep metric learning without triplet sampling
Qian, Q., Shang, L., Sun, B., Hu, J., Li, H., and Jin, R · 2019
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Deep-metric-learning-baselines
Roth, K. and Brattoli, B · 2019
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Mic: Mining interclass characteristics for improved metric learning
Roth, K., Brattoli, B., and Ommer, B · 2019
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Divide and conquer the embedding space for metric learning
Sanakoyeu, A., Tschernezki, V., Buchler, U., and Ommer, B · 2019
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Small-gan: Speeding up gan training using core-sets
Sinha, S., Zhang, H., Goyal, A., Bengio, Y., Larochelle, H., and Odena, A · 2019
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Signal-to-noise ratio: A robust distance metric for deep metric learning
Yuan, T., Deng, W., Tang, J., Tang, Y., and Chen, B · 2019
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Hardness-aware deep metric learning
Zheng, W., Chen, Z., Lu, J., and Zhou, J · 2019
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Fantastic generalization measures and where to find them
Jiang*, Y., Neyshabur*, B., Krishnan, D., Mobahi, H., and Bengio, S · 2020
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Coresets for accelerating incremental gradient methods, 2020
Mirzasoleiman, B., Bilmes, J., and Leskovec, J · 2020
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A metric learning reality check, 2020
Musgrave, K., Belongie, S., and Lim, S.-N · 2020
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Pads: Policy-adapted sampling for visual similarity learning, 2020
Roth, K., Milbich, T., and Ommer, B · 2020
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