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ResNet(
  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (relu): ReLU(inplace)
  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
  (block1): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

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Prerequisite: gensim.

Two scripts under src/ that you need to look at:

1. generate_embeddings.py: Creates dataloaders for embedded sentences using fasttext model trained on CUB dictionary. The train_loader and test_loader will return dataB of length 2: dataB[0]: [batch_size, sentence_length, embedding_vector_size] dataB[1]: [batch_size], original sentence length before truncation or padding (you can probably ignore this one, but I kept it there just in case you need the original length to truncate the sentence when calculating correlations)

2. coherence.py: this one is pretty much ready to go, it is defaulted to load the trained cub model under expeirments/ft_obj. You just need to import the CCA module (see usage in line 65, 66 and 77) that can be called with:

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X = blah, Y = blah
ones = np.ones(X.shape) # X and Y must be of the same shape
XX = np.diag(np.diag(X.dot(X.T))).dot(ones)
YY = ones.dot(np.diag(np.diag(Y.dot(Y.T))))
XY = X.dot(Y.T)
C = XX + YY - 2*XY

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# MNIST model specification

import torch
import torch.distributions as dist
import torch.nn as nn
import torch.nn.functional as F
from numpy import prod, sqrt
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from torchvision.utils import save_image, make_grid

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Paper + Code release!  We propose Fish, an effective algorithm for domain generalisation. It learns invariant features by maximising gradient inner product across domains.

Paper: https://arxiv.org/abs/2104.09937
Code: https://github.com/YugeTen/fish

Work done during internship at FAIR with syhw. A thread [insert FIgure1]

======

Our main idea: if the gradients of different domains point in similar directions, taking either gradient step improves the model's performance on *both* domains, indicating that the features learned by either gradient step are invariant across them.

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import os
import io
import subprocess
import numpy as np
from scipy.sparse.linalg import svds
from scipy.io import loadmat

import torch
from torchvision import datasets, transforms

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import os
import io
import subprocess
import numpy as np
from scipy.sparse.linalg import svds
from scipy.io import loadmat

import torch
from torchvision import datasets, transforms

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