# persistent contrastive divergence

What PIRL does differently is that it doesn’t use the direct output of the convolutional feature extractor. Contrastive Methods that push down the energy of training data points, $F(x_i, y_i)$, while pushing up energy on everywhere else, $F(x_i, y’)$. We show how these ap-proaches are related to each other and discuss the relative merits of each approach. Persistent Contrastive Divergence (PCD) Whereas CD k has some disadvantages and is not ex act, other methods are . The most commonly used learning algorithm for restricted Boltzmann machines is contrastive divergence which starts a Markov chain at a data point and runs the chain for only a few iterations to get a cheap, low variance estimate of the sufficient statistics under the model. To alleviate this problem, we explore the use of tempered Markov Chain Monte-Carlo for sampling in RBMs. $$\gdef \E {\mathbb{E}}$$ We study three of these methods, Contrastive Divergence (CD) and its refined variants Persistent CD (PCD) and Fast PCD (FPCD). If the energy we get is lower, we keep it. Contrastive divergence is an approximate ML learning algorithm pro-posed by Hinton (2001). It is compared to some standard Contrastive Divergence and Pseudo-Likelihood algorithms on the tasks of modeling and classifying various types of data. gorithm, named Persistent Contrastive Di-vergence, is diﬀerent from the standard Con-trastive Divergence algorithms in that it aims to draw samples from almost exactly the model distribution. $$\gdef \D {\,\mathrm{d}}$$ Researchers have found empirically that applying contrastive embedding methods to self-supervised learning models can indeed have good performances which rival that of supervised models. The Persistent Contrastive Divergence Because the probability distribution is always normalized to sum/integrate to 1, comparing the ratio between any two given data points is more useful than simply comparing absolute values. Note: Side effect occurs (updating weights). 5 0 obj 10 is the negative log likelihood (minus the ﬁxed entropy of P). In week 7’s practicum, we discussed denoising autoencoder. $$\gdef \R {\mathbb{R}}$$ One problem is that in a high dimensional continuous space, there are uncountable ways to corrupt a piece of data. In a mini-batch, we will have one positive (similar) pair and many negative (dissimilar) pairs. We hope that our model can produce good features for computer vision that rival those from supervised tasks. This is done by maintaining a set of \fantasy particles" v, h during the whole training. The model tends to learn the representation of the data by reconstructing corrupted input to the original input. Using Persistent Contrastive Divergence Showing 1-12 of 12 messages. Conceptually, contrastive embedding methods take a convolutional network, and feed $x$ and $y$ through this network to obtain two feature vectors: $h$ and $h’$. Maximum Likelihood method probabilistically pushes down energies at training data points and pushes everywhere else for every other value of $y’\neq y_i$. Tieleman, Tijmen. tic approximation procedure known as persistent contrastive divergence. Contrastive Divergence or Persistent Contrastive Divergence are often used for training the weights of Restricted Boltzmann machines. a positive pair), we want their feature vectors to be as similar as possible. It is well-known that CD has a number of shortcomings, and its approximation to the gradient has several drawbacks. We will briefly discuss the basic idea of contrastive divergence. Eventually, they will find low energy places in our energy surface and will cause them to be pushed up. share | improve this answer | follow | edited Jan 25 '19 at 1:40. Consequently, the persistent CD max- Please refer back to last week (Week 7 notes) for this information, especially the concept of contrastive learning methods. <> Tieleman (2008) showed that better learning can be achieved by estimating the model's statistics using a small set of persistent … Therefore, PIRL also uses a cached memory bank. Tieleman (2008) showed that better learning can be achieved by estimating the model’s statistics using a small set of persistent ”fantasy particles ” … Maximizing a softmax score means minimizing the rest of the scores, which is exactly what we want for an energy-based model. ��ٛ���n���q��V������[��E��� One of which is methods that are similar to Maximum Likelihood method, which push down the energy of data points and push up everywhere else. However, there are several problems with denoising autoencoders. stream Besides, corrupted points in the middle of the manifold could be reconstructed to both sides. Args: input_data (torch.tensor): Input data for CD algorithm. As seen in the figure above, MoCo and PIRL achieve SOTA results (especially for lower-capacity models, with a small number of parameters). These particles are moved down on the energy surface just like what we did in the regular CD. Overcoming these defects has been the basis of much research and new algorithms have been devised, such as persistent CD. This method allows us to push down on the energy of similar pairs while pushing up on the energy of dissimilar pairs. SimCLR shows better results than previous methods. Bibliographic details on Adiabatic Persistent Contrastive Divergence Learning. We feed these to our network above, obtain feature vectors $h$ and $h’$, and now try to minimize the similarity between them. Recent results (on ImageNet) have shown that this method can produce features that are good for object recognition that can rival the features learned through supervised methods. We then compute the score of a softmax-like function on the positive pair. K�N�P@u������oh/&��� �XG�聀ҫ! Parameters are estimated using Stochastic Maximum Likelihood (SML), also known as Persistent Contrastive Divergence (PCD) [2]. learning_rate float, default=0.1. $$\gdef \pd #1 #2 {\frac{\partial #1}{\partial #2}}$$ ��������Z�u~*]��?~y�����r�Ρ��A�]�zx��HT��O#�Pyi���fޱ!l�=��F��{\E�����=-���qxͦI� �z�� �vކ�K/ ��#�n�h����ݭ��vJwѐa��K�j8�OHpR���N��S��� ��K��!���:��G|��e +�+m?W�!�N����as�[������X7퀰�큌��p�V7 The final loss function, therefore, allows us to build a model that pushes the energy down on similar pairs while pushing it up on dissimilar pairs. This allows the particles to explore the space more thoroughly. Persistent Contrastive Divergence for RBMs. If you want to learn more about the mathematics behind this (Markov chains) and on the application to RBMs (contrastive divergence and persistent contrastive divergence), you might find this and this document helpful - these are some notes that I put together while learning about this. Empiri- cal results on various undirected models demon-strate that the particle ﬁltering technique we pro-pose in this paper can signiﬁcantly outperform MCMC-MLE. This is the case of Restricted Boltzmann Machines (RBM) and its learning algorithm Contrastive Divergence (CD). References. - Persistent Contrastive Divergence (PCD): Choose persistent_chain = True. %�쏢 The persistent contrastive divergence algorithm was further refined in a variant called fast persistent contrastive divergence (FPCD) [10]. In SGD, it can be difficult to consistently maintain a large number of these negative samples from mini-batches. The technique uses a sophisticated data augmentation method to generate similar pairs, and they train for a massive amount of time (with very, very large batch sizes) on TPUs. “Training Products of Experts by Minimizing Contrastive Divergence.” Neural Computation 14 (8): 1771–1800. The time complexity of this implementation is O(d ** 2) assuming d ~ n_features ~ n_components. In self-supervised learning, we use one part of the input to predict the other parts. Persistent Contrastive Divergence (PCD) is obtained from CD approximation by replacing the sample by a sample from a Gibbs chain that is independent of the sample of the training distribution. PIRL is starting to approach the top-1 linear accuracy of supervised baselines (~75%). In fact, it reaches the performance of supervised methods on ImageNet, with top-1 linear accuracy on ImageNet. More specifically, we train the system to produce an energy function that grows quadratically as the corrupted data move away from the data manifold. :˫*�FKarV�XD;/s+�$E~ �(!�q�؇��а�eEE�ϫ � �in�Q ��u ��ˠ � ��ÿ' However, we also have to push up on the energy of points outside this manifold. Instead of starting a new chain each time the gradient is needed, and performing only one Gibbs sampling step, in PCD we keep a number of chains (fantasy particles) that are updated $$k$$ Gibbs steps after each weight update. Putting everything together, PIRL’s NCE objective function works as follows. Maximum Likelihood doesn’t “care” about the absolute values of energies but only “cares” about the difference between energy. Answer: With an L2 norm, it’s very easy to make two vectors similar by making them “short” (close to centre) or make two vectors dissimilar by making them very “long” (away from the centre). Question: Why do we use cosine similarity instead of L2 Norm? $$\gdef \sam #1 {\mathrm{softargmax}(#1)}$$ There are many, many regions in a high-dimensional space where you need to push up the energy to make sure it’s actually higher than on the data manifold. Recently, Tieleman [8] proposed a faster alternative to CD, called Persistent Contrastive Divergence (PCD), which employs a persistent Markov chain to approximate hi. This paper studies the problem of parameter learning in probabilistic graphical models having latent variables, where the standard approach is the expectation maximization algorithm alternating expectation (E) and maximization (M) steps. However, the … By doing this, we lower the energy for images on the training data manifold. proposed in RBM. That completes this post on contrastive divergence. The most commonly used learning algorithm for restricted Boltzmann machines is contrastive divergence which starts a Markov chain at a data point and runs the chain for only a few iterations to get a cheap, low variance estimate of the sufficient statistics under the model. Thus, in every iteration, we take the result from the previous iteration, run one Gibbs sampling step and save the result as … This is because the L2 norm is just a sum of squared partial differences between the vectors. $$\gdef \vect #1 {\boldsymbol{#1}}$$ Parameters n_components int, default=256. the parameters, measures the departure As you increase the dimension of the representation, you need more and more negative samples to make sure the energy is higher in those places not on the manifold. In a continuous space, we first pick a training sample$y$and lower its energy. �J�[�������f�. This corresponds to standard CD without reinitializing the visible units of the Markov chain with a training sample each time we want to draw a sample . If the input space is discrete, we can instead perturb the training sample randomly to modify the energy. They apply the mean-ﬁeld approach in E step, and run an incomplete Markov chain (MC) only few cycles in M step, instead of running the chain until it converges or mixes. Thus, using cosine similarity forces the system to find a good solution without “cheating” by making vectors short or long. Download PDF: Sorry, we are unable to provide the full text but you may find it at the following location(s): http://arxiv.org/pdf/1605.0817... (external link) 7[�� /^�㘣};a�/i[օX!�[ܢ3���e��N�f3T������}>�? Persistent Contrastive Divergence addresses this. The system uses a bunch of “particles” and remembers their positions. The idea behind persistent contrastive divergence (PCD), proposed first in , is slightly different. So there is no guarantee that we can shape the energy function by simply pushing up on lots of different locations. $$\gdef \relu #1 {\texttt{ReLU}(#1)}$$ In the next post, I will show you an alternative algorithm that has gained a lot of popularity called persistent contrastive divergence (PCD), before we finally set out to implement an restricted Boltzmann machine on a GPU using the TensorFlow framework. The system uses a bunch of “particles” and remembers their positions. You can help us understand how dblp is used and perceived by answering our user survey (taking 10 to 15 minutes). One of the refinements of contrastive divergence is persistent contrastive divergence. Using Fast Weights to Improve Persistent Contrastive Divergence where P is the distribution of the training data and Qθ is the model’s distribution. We will explore some of these methods and their results below. Adiabatic Persistent Contrastive Divergence Learning Jang, Hyeryung; Choi, Hyungwon; Yi, Yung; Shin, Jinwoo; Abstract. $$\gdef \matr #1 {\boldsymbol{#1}}$$ To do so, I effectively changed this line: cost,updates = rbm.get_cost_updates(learning_rate, persistent… Eventually, they will find low energy places in our energy surface and will cause them to be pushed up. !�ZH%mF)�.�Ӿ��#Bg�4�� ����W;�������r�G�?AH8�gikGCS*?zi As we have learned from the last lecture, there are two main classes of learning methods: To distinguish the characteristics of different training methods, Dr. Yann LeCun has further summarized 7 strategies of training from the two classes mention before. Keep doing so will eventually lower the energy of$y$. x��=˒���Y}D�5�2ޏ�ee{זC��Mn�������"{F"[����� �(Tw�HiC5kP@"��껍�F����77q�q��Fn^݈͟n�5�j�e4���77�Hx4=x}�����F�L���ݛ�����oaõqj�웛���85���E9 learning_rate (float): Learning rate decay_rate (float): Decay rate for weight updates. As a result, we choose a similarity metric (such as cosine similarity) and a loss function that maximizes the similarity between$h$and$h’$. We can understand PIRL more by looking at its objective function: NCE (Noise Contrastive Estimator) as follows. Otherwise, we discard it with some probability. by Charles Fries in 1945 and was later popularized by Robert Lado in the late 1950s (Mutema&Mariko, 2012). This will create flat spots in the energy function and affect the overall performance. Using Fast Weights to Improve Persistent Contrastive Divergence VideoLectures NET 2. In this manuscript we propose a new … non-persistent) Contrastive Divergence (CD) learning algorithms based on the stochas-tic approximation and mean-ﬁeld theories. Another problem with the model is that it performs poorly when dealing with images due to the lack of latent variables. However, the system does not scale well as the dimensionality increases. Dr. LeCun mentions that to make this work, it requires a large number of negative samples. training algor ithm for RBMs we appl ied persistent Contrastive Divergence learning ( Hinton et al., 2006 ) and the fast weights heuristics described in Section 2.1.2. We then compute the similarity between the transformed image’s feature vector ($I^t$) and the rest of the feature vectors in the minibatch (one positive, the rest negative). Viewed 3k times 9. One of these methods is PCD that is very popular [17]. Here we define the similarity metric between two feature maps/vectors as the cosine similarity. It instead defines different heads$f$and$g$, which can be thought of as independent layers on top of the base convolutional feature extractor. Since there are many ways to reconstruct the images, the system produces various predictions and doesn’t learn particularly good features. $$\gdef \V {\mathbb{V}}$$ These particles are moved down on the energy surface just like what we did in the regular CD. Persistent Contrastive Divergence could on the other hand suffer from high correlation between subsequent gradient estimates due to poor mixing of the … $$\gdef \N {\mathbb{N}}$$ The second divergence, which is being maxi-mized w.r.t. Read more in the User Guide. called Persistent Contrastive Divergence (PCD) solves the sampling with a related method, only that the negative par- ticle is not sampled from the positive particle, but rather One of the refinements of contrastive divergence is persistent contrastive divergence. So we also generate negative samples ($x_{\text{neg}}$,$y_{\text{neg}}$), images with different content (different class labels, for example). Because$x$and$y$have the same content (i.e. Hinton, Geoffrey E. 2002. Instead of running a (very) short Gibbs sampler once for every iteration, the algorithm uses the final state of the previous Gibbs sampler as the initial start for the next iteration. Active 7 months ago. For that sample, we use some sort of gradient-based process to move down on the energy surface with noise. Your help is highly appreciated! Persistent hidden chains are used during negative phase in stead of hidden states at the end of positive phase. 1. We can then update the parameter of our energy function by comparing$y$and the contrasted sample$\bar y$with some loss function. Dr. LeCun believes that SimCLR, to a certain extend, shows the limit of contrastive methods. $$\gdef \set #1 {\left\lbrace #1 \right\rbrace}$$, Contrastive methods in self-supervised learning. Persistent Contrastive Divergence. Tieleman proposed to use the final samples from the previous MCMC chain at each mini-batch instead of the training points, as the initial state of the MCMC chain at each mini-batch. $$\gdef \deriv #1 #2 {\frac{\D #1}{\D #2}}$$ In contrastive methods, we push down on the energy of observed training data points ($x_i$,$y_i$), while pushing up on the energy of points outside of the training data manifold. 4$\begingroup$When using the persistent CD learning algorithm for Restricted Bolzmann Machines, we start our Gibbs sampling chain in the first iteration at a data point, but contrary to normal CD, in following iterations we don't start over our chain. We suspect that this property hinders RBM training methods such as the Contrastive Divergence and Persistent Contrastive Divergence algorithm that rely on Gibbs sampling to approximate the likelihood gradient. Persistent Contrastive Divergence. Using Persistent Contrastive Divergence: Andy: 6/23/11 1:06 PM: Hi there, I wanted to try Persistent Contrastive Divergence on the problem I have been working on, using code based on the DBN theano tutorial. Architectural Methods that build energy function$F$which has minimized/limited low energy regions by applying regularization. Contrastive Divergence is claimed to benefit from low variance of the gradient estimates when using stochastic gradients. The most commonly used learning algorithm for restricted Boltzmann machines is contrastive divergence which starts a Markov chain at a data point and runs the chain for only a few iterations to get a cheap, low variance estimate of the sufficient statistics under the model. Contrastive divergence (CD) is another model that learns the representation by smartly corrupting the input sample. Contrastive Analysis Hypothesis (CAH) was formulated . Dr. LeCun spent the first ~15 min giving a review of energy-based models. We call this a positive pair. There are other contrastive methods such as contrastive divergence, Ratio Matching, Noise Contrastive Estimation, and Minimum Probability Flow. Number of binary hidden units. Consider a pair ($x$,$y$), such that$x$is an image and$y$is a transformation of$x$that preserves its content (rotation, magnification, cropping, etc.). Contrastive Divergence (CD) and Persistent Contrastive Divergence (PCD) are popular methods for training the weights of Restricted Boltzmann Machines. The ﬁrst term in Eq. %PDF-1.2 Ask Question Asked 6 years, 7 months ago. To a certain extend, shows the limit of contrastive methods such as contrastive.. Discussed denoising autoencoder dissimilar pairs memory bank ~ n_components number of negative samples from mini-batches affect the overall.. Putting everything together, PIRL also uses a bunch of “ particles ” and remembers their positions as cosine... �������R�G�? AH8�gikGCS *? zi K�N�P @ u������oh/ & ��� �XG�聀ҫ negative phase stead. 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Robert Lado in the regular CD ( torch.tensor ): Choose persistent_chain =.! Solution without “ cheating ” by making vectors short or long an approximate learning! To be pushed up model can produce good features � �in ` �Q ��u. 1945 and was later popularized by Robert Lado in the energy function and affect the performance... Divergence and Pseudo-Likelihood algorithms on the energy, PIRL also uses a bunch of “ particles and., with top-1 linear accuracy on ImageNet, with top-1 linear accuracy on ImageNet eventually lower the we! Maximum Likelihood doesn ’ t learn particularly good features for computer vision that rival those from supervised tasks when with! ) and its learning algorithm contrastive Divergence ( CD ) learning algorithms based on energy... Was further refined in a variant called Fast Persistent contrastive Divergence SML ), we want their feature to!, shows the limit of contrastive Divergence persistent contrastive divergence PCD ) are popular methods training...