Our paper presents new state-of-the-art results on several benchmarks using the models learned from JFT-300M. /Resources 17 0 R q >> (2014) Tj
/R13 7.97010 Tf without changes to the underlying models or training and regularization techniques (beyond expanded capacity) demonstrates that q /ColorSpace << 3513.59000 3852.10000 3513.59000 3860.11000 3516.29000 3868.63000 c BT
W /R22 19 0 R Hierarchical latents improve memory and compute costs (primarily by reducing the parametric budget of the first linear layer), provide a modest performance improvement of around 4%, and improve training speed by a further 18%. f n 5333.02000 5499.43000 l Google Brain has released the pre-trained models and fine-tuning code for Big Transfer (BiT), a deep-learning computer vision model. >> /CS /DeviceRGB The recent progress in self-supervised representation learning for images showed impressive results on down-stream tasks. q /R13 9.96260 Tf Additionally, the generated image can be manipulated to exaggerate characteristic attributes of fake images. 3513.59000 3836.31000 l
Q This paper was accepted at ICCV, and they even published a blog post about the dataset. 3544.84000 3891.37000 3552.86000 3891.37000 3568.89000 3891.37000 c The spikes in D’s spectra might suggest that it periodically receives very large gradients, but we observe that the Frobenius norms are smooth (Appendix F), suggesting that this effect is primarily concentrated on the top few singular directions. 1 0 0 1 337.10300 540.57900 Tm We also successfully train BigGANs on ImageNet at 256×256 and 512×512 resolution, and achieve IS and FID of 233.0 and 9.3 at 256×256 and IS and FID of 241.4 and 10.9 at 512×512. /R19 9.96260 Tf 4765.56000 4003.30000 4765.56000 4033.08000 4784.28000 4051.45000 c /R22 19 0 R 14.94410 0 Td /R224 224 0 R /R29 gs /R22 cs Keras resnet 101. S /ExtGState << /R22 cs /R196 234 0 R /R199 244 0 R W Unsupervised representation learning with deep convolutional /Predictor 15 Quantitative results are presented in Table 3. /ExtGState << This provides direct visual grounding of this relation. The Cramer distance as a solution to biased Wasserstein Later they move the mouse from the woman to the balloon following its string, saying “holding”. 10 0 0 10 0 0 cm better base model. q In addition to 45 workshops and 16 tutorials. v3 3006.08000 697.42600 2401 4802 re During training, the images are resized and cropped to a square with a randomly chosen size, and randomly h-flipped.
is indeed memorizing the training set; 11.95510 -13.86170 Td 0 0 0 SCN >>
.
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